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                    <h1 class="text-lg md:text-xl font-bold text-gray-800">arXiv 每日论文精选</h1>
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                        <i class="fa fa-calendar-o mr-1"></i>2025-11-27
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                    <span class="text-gray-500 mr-1"><i class="fa fa-file-text-o"></i> 总论文数:</span>
                    <span id="total-papers" class="font-semibold text-primary">144</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-star"></i> 精选论文数:</span>
                    <span id="selected-papers" class="font-semibold text-accent">19</span>
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                    <span class="text-gray-500 mr-1"><i class="fa fa-line-chart"></i> 平均评分:</span>
                    <span id="avg-score" class="font-semibold text-secondary">2.5</span>
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                <span id="display-count" class="font-medium">显示 144 篇论文 (共 144 篇)</span>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21394v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>RIA：一种用于优化列表式CTR预测的排序融合方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            RIA: A Ranking-Infused Approach for Optimized listwise CTR Prediction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoxiao Zhang, Tan Qu, Ao Li, DongLin Ni, Qianlong Xie, Xingxing Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究推荐系统中重排序与排序解耦导致的组合稀疏性和表示能力受限问题，核心思想是通过用户-候选项双Transformer、上下文感知偏好学习、层次化项目依赖建模和嵌入缓存模块，构建端到端的统一框架实现排序与重排序的表示共享。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对推荐系统中的重排序问题，提出统一架构整合点排序和列表排序，核心关注推荐质量提升和延迟约束，与推荐系统核心领域高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:45:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21394v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21394v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reranking improves recommendation quality by modeling item interactions. However, existing methods often decouple ranking and reranking, leading to weak listwise evaluation models that suffer from combinatorial sparsity and limited representational power under strict latency constraints. In this paper, we propose RIA (Ranking-Infused Architecture), a unified, end-to-end framework that seamlessly integrates pointwise and listwise evaluation. RIA introduces four key components: (1) the User and Candidate DualTransformer (UCDT) for fine-grained user-item-context modeling; (2) the Context-aware User History and Target (CUHT) module for position-sensitive preference learning; (3) the Listwise Multi-HSTU (LMH) module to capture hierarchical item dependencies; and (4) the Embedding Cache (EC) module to bridge efficiency and effectiveness during inference. By sharing representations across ranking and reranking, RIA enables rich contextual knowledge transfer while maintaining low latency. Extensive experiments show that RIA outperforms state-of-the-art models on both public and industrial datasets, achieving significant gains in AUC and LogLoss. Deployed in Meituan advertising system, RIA yields a +1.69% improvement in Click-Through Rate (CTR) and a +4.54% increase in Cost Per Mille (CPM) in online A/B tests.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21389v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>FITRep：通过多模态大语言模型实现注意力引导的物品表示
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            FITRep: Attention-Guided Item Representation via MLLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Guoxiao Zhang, Ao Li, Tan Qu, Qianlong Xie, Xingxing Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究在线平台中视觉和文本相似的近重复项导致的用户体验下降问题；核心方法是受特征整合理论启发，通过MLLM提取层次化语义概念并保持结构关系，构建白箱化的物品表示框架进行细粒度去重。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接针对广告系统中的重复项问题，提出基于MLLM的注意力引导表示框架，属于LLM在推荐/广告领域的直接应用创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:38:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21389v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21389v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
                     查看完整摘要 <i class="fa fa-chevron-down ml-1 text-xs"></i> 
                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Online platforms usually suffer from user experience degradation due to near-duplicate items with similar visuals and text. While Multimodal Large Language Models (MLLMs) enable multimodal embedding, existing methods treat representations as black boxes, ignoring structural relationships (e.g., primary vs. auxiliary elements), leading to local structural collapse problem. To address this, inspired by Feature Integration Theory (FIT), we propose FITRep, the first attention-guided, white-box item representation framework for fine-grained item deduplication. FITRep consists of: (1) Concept Hierarchical Information Extraction (CHIE), using MLLMs to extract hierarchical semantic concepts; (2) Structure-Preserving Dimensionality Reduction (SPDR), an adaptive UMAP-based method for efficient information compression; and (3) FAISS-Based Clustering (FBC), a FAISS-based clustering that assigns each item a unique cluster id using FAISS. Deployed on Meituan's advertising system, FITRep achieves +3.60% CTR and +4.25% CPM gains in online A/B tests, demonstrating both effectiveness and real-world impact.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21121v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>超越补丁聚合：用于视觉增强文档检索的三通道金字塔索引
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Beyond Patch Aggregation: 3-Pass Pyramid Indexing for Vision-Enhanced Document Retrieval
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anup Roy, Rishabh Gyanendra Upadhyay, Animesh Rameshbhai Panara, Robin Mills
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究视觉增强文档检索中的效率与灵活性平衡问题，核心思想是构建三阶段金字塔索引框架，通过全局页面摘要、章节标题和视觉热点等多层次语义向量实现轻量级检索代理，同时保持模型无关性。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的视觉优先检索框架和金字塔索引方法直接适用于搜索和推荐系统，其处理异构数据（图像、文本、布局）的统一建模思想与VLM类比高度相关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:18:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21121v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21121v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.IR</span><span class="category-tag">cs.AI</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Document centric RAG pipelines usually begin with OCR, followed by brittle heuristics for chunking, table parsing, and layout reconstruction. These text first workflows are costly to maintain, sensitive to small layout shifts, and often lose the spatial cues that contain the answer. Vision first retrieval has emerged as a strong alternative. By operating directly on page images, systems like ColPali and ColQwen preserve structure and reduce pipeline complexity while achieving strong benchmark performance. However, these late interaction models tie retrieval to a specific vision backbone and require storing hundreds of patch embeddings per page, creating high memory overhead and complicating large scale deployment. We introduce VisionRAG, a multimodal retrieval system that is OCR free and model agnostic. VisionRAG indexes documents directly as images, preserving layout, tables, and spatial cues, and builds semantic vectors without committing to a specific extraction. Our three pass pyramid indexing framework creates vectors using global page summaries, section headers, visual hotspots, and fact level cues. These summaries act as lightweight retrieval surrogates. At query time, VisionRAG retrieves the most relevant pages using the pyramid index, then forwards the raw page image encoded as base64 to a multimodal LLM for final question answering. During retrieval, reciprocal rank fusion integrates signals across the pyramid to produce robust ranking. VisionRAG stores only 17 to 27 vectors per page, matching the efficiency of patch based methods while staying flexible across multimodal encoders. On financial document benchmarks, it achieves 0.8051 accuracy at 10 on FinanceBench and 0.9629 recall at 100 on TAT DQA. These results show that OCR free, summary guided multimodal retrieval is a practical and scalable alternative to traditional text extraction pipelines.
                </div>
            </details>
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21416v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Odin：面向文本丰富网络表示学习的定向双模块集成
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Odin: Oriented Dual-module Integration for Text-rich Network Representation Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaifeng Hong, Yinglong Zhang, Xiaoying Hong, Xuewen Xia, Xing Xu
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究文本属性图的表示学习问题，核心思想是通过定向双模块机制在特定Transformer层注入图结构，实现层次化结构抽象与语义表示的端到端对齐，避免传统GNN的过平滑问题。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出面向异构数据的双模态统一建模框架，直接对应VLM类比研究重点，同时在Transformer架构效率优化和结构-文本融合方面具有重要创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:07:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21416v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21416v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
                 <summary class="text-sm text-primary cursor-pointer"> 
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Text-attributed graphs require models to effectively combine strong textual understanding with structurally informed reasoning. Existing approaches either rely on GNNs--limited by over-smoothing and hop-dependent diffusion--or employ Transformers that overlook graph topology and treat nodes as isolated sequences. We propose Odin (Oriented Dual-module INtegration), a new architecture that injects graph structure into Transformers at selected depths through an oriented dual-module mechanism.Unlike message-passing GNNs, Odin does not rely on multi-hop diffusion; instead, multi-hop structures are integrated at specific Transformer layers, yielding low-, mid-, and high-level structural abstraction aligned with the model's semantic hierarchy. Because aggregation operates on the global [CLS] representation, Odin fundamentally avoids over-smoothing and decouples structural abstraction from neighborhood size or graph topology. We further establish that Odin's expressive power strictly contains that of both pure Transformers and GNNs.To make the design efficient in large-scale or low-resource settings, we introduce Light Odin, a lightweight variant that preserves the same layer-aligned structural abstraction for faster training and inference. Experiments on multiple text-rich graph benchmarks show that Odin achieves state-of-the-art accuracy, while Light Odin delivers competitive performance with significantly reduced computational cost. Together, Odin and Light Odin form a unified, hop-free framework for principled structure-text integration. The source code of this model has been released at https://github.com/hongkaifeng/Odin.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21408v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>主观深度与时间尺度变换器：学习在何处及何时进行计算
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Subjective Depth and Timescale Transformers: Learning Where and When to Compute
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Frederico Wieser, Martin Benfeghoul, Haitham Bou Ammar, Jun Wang, Zafeirios Foun...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究标准Transformer架构中计算资源刚性分配导致的效率限制问题，核心思想是引入基于贝叶斯惊喜信号的动态计算路由机制，通过决策层和动态层的交替设计，让模型自主学习在何处和何时进行计算。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文在Transformer架构效率优化方面提出创新方法，通过贝叶斯惊喜信号动态路由计算，直接适用于大规模推荐和搜索系统的高效部署。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:00:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21408v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21408v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.IT</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The rigid, uniform allocation of computation in standard Transformer (TF) architectures can limit their efficiency and scalability, particularly for large-scale models and long sequences. Addressing this, we introduce Subjective Depth Transformers (SDT) and Subjective Timescale Transformers (STT), two distinct architectures that leverage Bayesian surprise signals to dynamically route computation, learning where and when to compute within decoder-only TFs. SDT augments a decoder-only stack with alternating Decision and Dynamic layers: a Decision layer computes a full block 'posterior' and a lightweight 'prior,' while a Dynamic layer employs fixed-capacity Top-K routing based on Bayesian surprise (Expected and Unexpected Change), maintaining a static compute graph. STT extends this conditional computation to the temporal domain: a transition network predicts residual updates, forming a temporal 'change hypothesis' that informs a router to dynamically execute or bypass TF blocks for each token, managing KV-cache contributions. Both architectures exhibit the predicted shift from novelty to prediction driven gating over training, suggesting alignment with surprise based principles. While operating at reduced capacity, they offer preliminary insights into the compute-accuracy trade-offs of conditional computation. The proposed architectures establish a flexible framework for efficiency, reducing self-attention computation by 75% and KV-cache requirements by 50% within each compute skipping layer, setting a pathway for more efficient models.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21056v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>后训练大语言模型离线数据选择与在线自优化生成统一理解
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            A Unified Understanding of Offline Data Selection and Online Self-refining Generation for Post-training LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Quan Xiao, Tianyi Chen
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何通过优化视角统一解决LLM下游任务适配中的离线数据选择和在线自优化生成问题；核心方法是采用双层数据选择框架，为每个问题和响应分配学习权重，将在线自优化视为选择最适合验证数据的模型适配步骤。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文从优化视角统一理解离线数据选择和在线自优化生成，为核心LLM技术提供了数据质量增强的关键方法，直接适用于搜索推荐系统的模型适配。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:48:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21056v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21056v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span><span class="category-tag">math.OC</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Offline data selection and online self-refining generation, which enhance the data quality, are crucial steps in adapting large language models (LLMs) to specific downstream tasks. We tackle offline data selection and online self-refining generations through an optimization perspective. Specifically, bilevel data selection is used for offline data selection with respect to the validation dataset, and we treat online self-refining generation as a model adaptation step of selecting the model trained on current responses that best fits the validation data. Our framework offers a unified understanding of offline data selection and self-refining generation by assigning a learned data weight to each question and response, either explicitly or implicitly. For the first time, we theoretically demonstrate the effectiveness of the bilevel data selection framework and demonstrate its performance gains over unfiltered direct mixing baselines. By combining offline data with validation-weighted online generations, our method enhances fine-tuning performance. Experiments on quality enhancement and safety-aware LLM fine-tuning validate its effectiveness.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21038v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>上下文学习中的语义锚点：为何小型LLM无法翻转其标签
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Semantic Anchors in In-Context Learning: Why Small LLMs Cannot Flip Their Labels
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anantha Padmanaban Krishna Kumar
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究上下文学习能否覆盖预训练的标签语义，核心发现是ICL主要调整输入在稳定语义方向上的投影，而非灵活重映射标签含义，揭示了小规模LLM无法通过少量样本提示覆盖预训练语义锚点的根本限制。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文深入探讨了上下文学习与预训练语义的交互机制，直接关系到LLM在推荐搜索系统中处理标签语义的核心能力限制。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:14:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21038v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21038v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Can in-context learning (ICL) override pre-trained label semantics, or does it merely refine an existing semantic backbone? We address this question by treating LLMs as prompt-induced classifiers and contrasting their behavior under \emph{natural} demonstrations (with correct labels) and \emph{inverted} demonstrations (systematically flipping label meanings). We decompose ICL behavior into three alignment metrics (truth, prior, and prompt alignment) and introduce a semantic override rate, defined as correctness under flipped semantics. Across eight classification tasks and eight open-source LLMs (1--12B parameters), we find consistent evidence for a semantic anchor view. With natural demonstrations, ICL improves accuracy while maintaining strong prior alignment; most correct predictions coincide with zero-shot behavior, even when the prior is weak. With inverted demonstrations, models cannot learn coherent anti-semantic classifiers: prompt alignment increases only by sacrificing accuracy, and semantic override rates remain exactly zero in our few-shot 1--12B setting. Rather than flexibly remapping label meanings, ICL primarily adjusts how inputs project onto stable semantic directions learned during pre-training, clarifying fundamental limits of few-shot prompting and suggesting that overriding label semantics at these scales requires interventions beyond ICL. All code is available at: https://github.com/AnanthaPadmanaban-KrishnaKumar/semantic-anchors-icl.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21331v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>多多益善：基于对比学习的高阶多模态对齐融合方法
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            The More, the Merrier: Contrastive Fusion for Higher-Order Multimodal Alignment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Stefanos Koutoupis, Michaela Areti Zervou, Konstantinos Kontras, Maarten De Vos,...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究多模态机器学习中的联合表示学习问题，核心思想是通过对比融合框架同时嵌入单个模态及其融合组合到统一表示空间，利用融合模态对比项捕获仅靠成对对齐无法恢复的高阶依赖关系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的多模态对比融合框架直接适用于推荐系统中的异构数据对齐，其高阶交互建模思想与VLM异构数据处理理念高度契合。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:25:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21331v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21331v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Learning joint representations across multiple modalities remains a central challenge in multimodal machine learning. Prevailing approaches predominantly operate in pairwise settings, aligning two modalities at a time. While some recent methods aim to capture higher-order interactions among multiple modalities, they often overlook or insufficiently preserve pairwise relationships, limiting their effectiveness on single-modality tasks. In this work, we introduce Contrastive Fusion (ConFu), a framework that jointly embeds both individual modalities and their fused combinations into a unified representation space, where modalities and their fused counterparts are aligned. ConFu extends traditional pairwise contrastive objectives with an additional fused-modality contrastive term, encouraging the joint embedding of modality pairs with a third modality. This formulation enables ConFu to capture higher-order dependencies, such as XOR-like relationships, that cannot be recovered through pairwise alignment alone, while still maintaining strong pairwise correspondence. We evaluate ConFu on synthetic and real-world multimodal benchmarks, assessing its ability to exploit cross-modal complementarity, capture higher-order dependencies, and scale with increasing multimodal complexity. Across these settings, ConFu demonstrates competitive performance on retrieval and classification tasks, while supporting unified one-to-one and two-to-one retrieval within a single contrastive framework.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21106v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>EM-KD：利用不平衡视觉令牌蒸馏高效多模态大语言模型
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>9/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            EM-KD: Distilling Efficient Multimodal Large Language Model with Unbalanced Vision Tokens
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ze Feng, Sen Yang, Boqiang Duan, Wankou Yang, Jingdong Wang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究高效多模态大语言模型中视觉令牌压缩导致信息丢失的问题，核心思想是通过曼哈顿距离和匈牙利匹配算法对齐师生模型的视觉令牌，然后提出视觉-语言亲和力蒸馏和视觉语义蒸馏两种策略来提升学生模型的细粒度视觉理解能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出针对不平衡视觉令牌的知识蒸馏方法，直接解决多模态模型效率问题，与VLM异构数据处理高度相关，对搜索推荐系统的多模态理解有重要应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:45:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21106v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21106v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Efficient Multimodal Large Language Models (MLLMs) compress vision tokens to reduce resource consumption, but the loss of visual information can degrade comprehension capabilities. Although some priors introduce Knowledge Distillation to enhance student models, they overlook the fundamental differences in fine-grained vision comprehension caused by unbalanced vision tokens between the efficient student and vanilla teacher. In this paper, we propose EM-KD, a novel paradigm that enhances the Efficient MLLMs with Knowledge Distillation. To overcome the challenge of unbalanced vision tokens, we first calculate the Manhattan distance between the vision logits of teacher and student, and then align them in the spatial dimension with the Hungarian matching algorithm. After alignment, EM-KD introduces two distillation strategies: 1) Vision-Language Affinity Distillation (VLAD) and 2) Vision Semantic Distillation (VSD). Specifically, VLAD calculates the affinity matrix between text tokens and aligned vision tokens, and minimizes the smooth L1 distance of the student and the teacher affinity matrices. Considering the semantic richness of vision logits in the final layer, VSD employs the reverse KL divergence to measure the discrete probability distributions of the aligned vision logits over the vocabulary space. Comprehensive evaluation on diverse benchmarks demonstrates that EM-KD trained model outperforms prior Efficient MLLMs on both accuracy and efficiency with a large margin, validating its effectiveness. Compared with previous distillation methods, which are equipped with our proposed vision token matching strategy for fair comparison, EM-KD also achieves better performance.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21689v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>ToolOrchestra：通过高效的模型与工具编排提升智能
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orchestration
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hongjin Su, Shizhe Diao, Ximing Lu, Mingjie Liu, Jiacheng Xu, Xin Dong, Yonggan ...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何通过小型编排器协调多个模型和工具来解决复杂智能任务；核心方法是使用强化学习训练小型编排器，通过结果、效率和用户偏好感知的奖励来优化工具选择和协调策略。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的小型编排器协调智能工具的方法直接适用于推荐和搜索系统中的模型编排与工具集成，其强化学习奖励机制对用户偏好建模有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21689v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21689v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.MA</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models are powerful generalists, yet solving deep and complex problems such as those of the Humanity's Last Exam (HLE) remains both conceptually challenging and computationally expensive. We show that small orchestrators managing other models and a variety of tools can both push the upper bound of intelligence and improve efficiency in solving difficult agentic tasks. We introduce ToolOrchestra, a method for training small orchestrators that coordinate intelligent tools. ToolOrchestra explicitly uses reinforcement learning with outcome-, efficiency-, and user-preference-aware rewards. Using ToolOrchestra, we produce Orchestrator, an 8B model that achieves higher accuracy at lower cost than previous tool-use agents while aligning with user preferences on which tools are to be used for a given query. On HLE, Orchestrator achieves a score of 37.1%, outperforming GPT-5 (35.1%) while being 2.5x more efficient. On tau2-Bench and FRAMES, Orchestrator surpasses GPT-5 by a wide margin while using only about 30% of the cost. Extensive analysis shows that Orchestrator achieves the best trade-off between performance and cost under multiple metrics, and generalizes robustly to unseen tools. These results demonstrate that composing diverse tools with a lightweight orchestration model is both more efficient and more effective than existing methods, paving the way for practical and scalable tool-augmented reasoning systems.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21613v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>超越URL：用于高效LLM预训练的元数据多样性与位置
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Beyond URLs: Metadata Diversity and Position for Efficient LLM Pretraining
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dongyang Fan, Diba Hashemi, Sai Praneeth Karimireddy, Martin Jaggi
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何通过多样化metadata提升LLM预训练效率，核心思想是识别细粒度metadata信息并通过前置、后置预测任务及可学习元标记来加速模型学习过程。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文直接研究LLM预训练效率提升方法，属于核心LLM技术进步，其metadata处理方法对推荐搜索系统中的异构数据处理具有直接借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:36:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21613v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21613v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Incorporating metadata in Large Language Models (LLMs) pretraining has recently emerged as a promising approach to accelerate training. However prior work highlighted only one useful signal-URLs, leaving open the question of whether other forms of metadata could yield greater benefits. In this study, we investigate a wider range of metadata types and find other types of metadata, such as fine-grained indicators of document quality that can also accelerate pretraining when prepended. We identify a common feature among effective metadata: they encode information at a finer granularity. We further introduce metadata appending as a means of improving training efficiency, where predicting an appropriate metadata as auxiliary task can help speed up pretraining. In addition, learnable meta-tokens trained with masked loss can recover part of the speedup by inducing quality-aware latent structure. Using probing, we analyze latent representations to understand how metadata shapes learning. Together, these results yield practical guidelines for integrating metadata to improve both the efficiency and effectiveness of LLM pretraining.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21399v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>训练内省行为：微调在70亿参数模型中诱导可靠的内部状态检测
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Training Introspective Behavior: Fine-Tuning Induces Reliable Internal State Detection in a 7B Model
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joshua Fonseca Rivera
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何通过训练使语言模型可靠检测内部注入的激活模式；核心方法是利用单令牌注入的微调技术，使模型学会识别和报告语义内容，实现内置的AI透明度能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究通过微调使LLM具备检测内部状态的能力，直接关联LLM透明度和内部表征理解，对推荐和搜索系统中模型可解释性有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:49:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21399v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21399v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Lindsey (2025) investigates introspective awareness in language models through four experiments, finding that models can sometimes detect and identify injected activation patterns -- but unreliably (~20% success in the best model). We focus on the first of these experiments -- self-report of injected "thoughts" -- and ask whether this capability can be directly trained rather than waiting for emergence. Through fine-tuning on transient single-token injections, we transform a 7B parameter model from near-complete failure (0.4% accuracy, 6.7% false positive rate) to reliable detection (85% accuracy on held-out concepts at α=40, 0% false positives). Our model detects fleeting "thoughts" injected at a single token position, retains that information, and reports the semantic content across subsequent generation steps. On this task, our trained model satisfies three of Lindsey's criteria: accuracy (correct identification), grounding (0/60 false positives), and internality (detection precedes verbalization). Generalization to unseen concept vectors (7.5pp gap) demonstrates the model learns a transferable skill rather than memorizing specific vectors, though this does not establish metacognitive representation in Lindsey's sense. These results address an open question raised by Lindsey: whether "training for introspection would help eliminate cross-model differences." We show that at least one component of introspective behavior can be directly induced, offering a pathway to built-in AI transparency.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21285v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>PEFT-Bench：参数高效微调方法基准测试
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            PEFT-Bench: A Parameter-Efficient Fine-Tuning Methods Benchmark
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Robert Belanec, Branislav Pecher, Ivan Srba, Maria Bielikova
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究参数高效微调方法评估标准不统一的问题，核心贡献是构建了统一的PEFT-Bench基准框架和综合考虑训练参数、推理速度、内存使用的PSCP评估指标。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于参数高效微调方法的基准测试，直接关联LLM效率提升技术，对推荐系统和搜索中的模型部署优化具有重要价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 11:18:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21285v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21285v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite the state-of-the-art performance of Large Language Models (LLMs) achieved on many tasks, their massive scale often leads to high computational and environmental costs, limiting their accessibility. Parameter-efficient fine-tuning (PEFT) methods address this challenge by reducing the number of trainable parameters while maintaining strong downstream performance. Despite the increased development in PEFT methods, current evaluations remain limited (in terms of evaluated models and datasets) and difficult to reproduce. To bridge this gap, we introduce PEFT-Bench, a unified end-to-end benchmark for evaluating diverse PEFT methods on autoregressive LLMs. We demonstrate its usage across 27 NLP datasets and 6 PEFT methods. To account for different PEFT training and inference factors, we also introduce the PEFT Soft Score Penalties (PSCP) metric, which takes trainable parameters, inference speed, and training memory usage into account.
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21631v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>Qwen3-VL技术报告
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Qwen3-VL Technical Report
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuai Bai, Yuxuan Cai, Ruizhe Chen, Keqin Chen, Xionghui Chen, Zesen Cheng, Lian...
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何构建更强大的视觉语言模型，核心方法是引入增强的interleaved-MRoPE进行时空建模、DeepStack集成实现多层级视觉特征对齐、以及文本时间对齐技术来提升多模态推理能力。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文在Transformer架构效率（MoE变体）和跨模态建模方面有显著创新，这些技术可直接应用于推荐系统的多模态内容理解和高效推理。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:59:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21631v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21631v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce Qwen3-VL, the most capable vision-language model in the Qwen series to date, achieving superior performance across a broad range of multimodal benchmarks. It natively supports interleaved contexts of up to 256K tokens, seamlessly integrating text, images, and video. The model family includes both dense (2B/4B/8B/32B) and mixture-of-experts (30B-A3B/235B-A22B) variants to accommodate diverse latency-quality trade-offs. Qwen3-VL delivers three core pillars: (i) markedly stronger pure-text understanding, surpassing comparable text-only backbones in several cases; (ii) robust long-context comprehension with a native 256K-token window for both text and interleaved multimodal inputs, enabling faithful retention, retrieval, and cross-referencing across long documents and videos; and (iii) advanced multimodal reasoning across single-image, multi-image, and video tasks, demonstrating leading performance on comprehensive evaluations such as MMMU and visual-math benchmarks (e.g., MathVista and MathVision). Architecturally, we introduce three key upgrades: (i) an enhanced interleaved-MRoPE for stronger spatial-temporal modeling across images and video; (ii) DeepStack integration, which effectively leverages multi-level ViT features to tighten vision-language alignment; and (iii) text-based time alignment for video, evolving from T-RoPE to explicit textual timestamp alignment for more precise temporal grounding. Under comparable token budgets and latency constraints, Qwen3-VL achieves superior performance in both dense and Mixture-of-Experts (MoE) architectures. We envision Qwen3-VL serving as a foundational engine for image-grounded reasoning, agentic decision-making, and multimodal code intelligence in real-world workflows.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21523v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>EoS-FM：专家模型集成能否作为通用特征提取器？
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>8/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            EoS-FM: Can an Ensemble of Specialist Models act as a Generalist Feature Extractor?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pierre Adorni, Minh-Tan Pham, Stéphane May, Sébastien Lefèvre
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何构建高效的地球观测基础模型，核心思想是通过分解训练过程为轻量级任务特定专家模型，实现模块化、可复用和可扩展的集成框架。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的专家模型集成框架直接关联Transformer架构效率优化和模块化设计，其轻量级任务分解方法对推荐系统中的多任务学习和联邦学习具有重要借鉴意义。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:52:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21523v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21523v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent advances in foundation models have shown great promise in domains such as natural language processing and computer vision, and similar efforts are now emerging in the Earth Observation community. These models aim to generalize across tasks with limited supervision, reducing the need for training separate models for each task. However, current strategies, which largely focus on scaling model size and dataset volume, require prohibitive computational and data resources, limiting accessibility to only a few large institutions. Moreover, this paradigm of ever-larger models stands in stark contrast with the principles of sustainable and environmentally responsible AI, as it leads to immense carbon footprints and resource inefficiency. In this work, we present a novel and efficient alternative: an Ensemble-of-Specialists framework for building Remote Sensing Foundation Models (RSFMs). Our method decomposes the training process into lightweight, task-specific ConvNeXtV2 specialists that can be frozen and reused. This modular approach offers strong advantages in efficiency, interpretability, and extensibility. Moreover, it naturally supports federated training, pruning, and continuous specialist integration, making it particularly well-suited for collaborative and resource-constrained settings. Our framework sets a new direction for building scalable and efficient RSFMs.
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</div>
<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21610v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>辅助指标有助于在真实场景中解码技能神经元
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Auxiliary Metrics Help Decoding Skill Neurons in the Wild
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yixiu Zhao, Xiaozhi Wang, Zijun Yao, Lei Hou, Juanzi Li
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何在大语言模型中识别编码特定技能的神经元。核心方法是利用外部标签和模型置信度等辅助指标与神经元激活相关联，无需手动标记聚合即可发现可解释的任务特定行为。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出利用辅助指标解码技能神经元的方法，直接关联LLM内部机制分析，对理解模型行为有重要意义，与核心LLM技术进展和应用相关。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:31:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21610v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21610v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
            </div>
            
            
            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models (LLMs) exhibit remarkable capabilities across a wide range of tasks, yet their internal mechanisms remain largely opaque. In this paper, we introduce a simple, lightweight, and broadly applicable method with a focus on isolating neurons that encode specific skills. Building upon prior work that identified "skill neurons" via soft prompt training on classification tasks, our approach extends the analysis to complex scenarios involving multiple skills. We correlate neuron activations with auxiliary metrics -- such as external labels and the model's own confidence score -- thereby uncovering interpretable and task-specific behaviors without the need for manual token aggregation. We empirically validate our method on tasks spanning open-ended text generation and natural language inference, demonstrating its ability to detect neurons that not only drive known skills but also reveal previously unidentified shortcuts in arithmetic reasoning on BigBench.
                </div>
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<div class="paper-card p-4 expanded">
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21437v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>大型语言模型中模型融合技术的系统性研究
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>7/10
        </span>
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        <div class="mb-2 text-base text-gray-700">
            A Systematic Study of Model Merging Techniques in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Oğuz Kağan Hitit, Leander Girrbach, Zeynep Akata
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究大型语言模型中的模型合并技术，核心发现是最简单的方法Task Arithmetic在LLM上表现最佳，而其他复杂方法通常导致性能下降，表明现有合并技术不能直接迁移到现代LLM。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文系统研究LLM模型合并技术，直接涉及核心LLM技术进展和Transformer架构效率优化，对推荐系统模型复用有重要参考价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:28:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21437v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21437v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Model merging combines multiple fine-tuned checkpoints into a single model without additional training, offering an attractive approach to reusing models and efficiently improving performance. However, it remains unclear whether the advantages reported for smaller models and classifiers generalize to LLMs. We present a large-scale, systematic evaluation of six state-of-the-art merging methods, including recent subspace methods, across four open-weight LLMs, twelve fine-tuned checkpoints per base model, and sixteen standard LLM benchmarks. Evaluating through standardized benchmarks, we measure both the probability that a merged model outperforms the base model and relative gains over the best individual checkpoint. Our results show that the oldest and simplest method, Task Arithmetic, is the only approach that reliably yields performance gains on LLMs. Other interference-aware and subspace merging methods typically result in significant performance drops. Our findings indicate that current merging techniques do not directly transfer to modern LLMs. This motivates the design of LLM-specific merging algorithms and merging-aware fine-tuning methods. Code will be released upon acceptance of this paper.
                </div>
            </details>
    </div>
</div>
<div class="paper-card p-4 expanded">
    <div class="flex justify-between items-start mb-2">
        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21188v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>AnchorOPT：面向自适应提示学习的动态锚点优化
            </a>
        </h3>
        <span class="score-badge bg-green-100 text-green-800">
            <i class="fa fa-star mr-1"></i>6/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            AnchorOPT: Towards Optimizing Dynamic Anchors for Adaptive Prompt Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zheng Li, Yibing Song, Xin Zhang, Lei Luo, Xiang Li, Jian Yang
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究静态锚点在提示学习中的灵活性不足问题，核心创新是提出动态锚点框架：通过任务数据学习锚点值，并优化锚点与软令牌之间的自适应位置关系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出的动态锚点优化方法属于Transformer架构效率优化范畴，其自适应学习机制对推荐系统中的特征交互建模有潜在启发价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:11:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21188v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21188v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
            </div>
            
            
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Existing prompt learning methods, which are built upon CLIP models, leverage textual tokens as anchors to guide the learnable soft tokens. This guidance improves CLIP generalizations. However, these anchors-static in both value and position-lack cross-task and stage-adaptive flexibility. To address this limitation, we propose AnchorOPT, a dynamic anchor-based prompt learning framework. Specifically, AnchorOPT introduces dynamism in two key dimensions: (i) anchor values eschew handcrafted explicit textual tokens (e.g., "shape", "color"), instead learning dynamically from task-specific data; and (ii) the positional relationship between anchor and soft tokens is no longer fixed but adaptively optimized via a learnable position matrix conditioned on the training stage and task context. Training occurs in two stages: we first learn the anchor tokens, then freeze and transfer them to the second stage for optimization of soft tokens and the position matrix. Extensive experiments demonstrate that using only a simple learnable anchor and position matrix achieves performance comparable to or exceeding some methods incorporating additional learnable modules or regularization techniques. As a plug-and-play module, AnchorOPT integrates seamlessly into existing frameworks, yielding consistent performance gains across diverse datasets. Code is publicly available at https://github.com/zhengli97/ATPrompt.
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        <h3 class="text-lg font-semibold text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21503v1" target="_blank" rel="noopener noreferrer">
                <i class="fa fa-star text-yellow-400 mr-1"></i>CanKD：基于交叉注意力的非局部操作用于基于特征的知识蒸馏
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            CanKD: Cross-Attention-based Non-local operation for Feature-based Knowledge Distillation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shizhe Sun, Wataru Ohyama
        </div>
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-lightbulb-o text-yellow-500 mr-1"></i>核心总结:</strong>
            <p class="text-gray-600 text-sm mt-1">论文研究如何改进基于特征的知识蒸馏方法，核心思想是利用跨注意力机制让学生特征图的每个像素动态考虑教师特征图的所有像素，实现非局部知识传递以更好地捕获像素间关系。</p>
        </div>
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的知识蒸馏技术，虽然使用了注意力机制，但应用场景与推荐、搜索、广告系统的核心问题关联度较低。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:38:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21503v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21503v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We propose Cross-Attention-based Non-local Knowledge Distillation (CanKD), a novel feature-based knowledge distillation framework that leverages cross-attention mechanisms to enhance the knowledge transfer process. Unlike traditional self-attention-based distillation methods that align teacher and student feature maps independently, CanKD enables each pixel in the student feature map to dynamically consider all pixels in the teacher feature map. This non-local knowledge transfer more thoroughly captures pixel-wise relationships, improving feature representation learning. Our method introduces only an additional loss function to achieve superior performance compared with existing attention-guided distillation methods. Extensive experiments on object detection and image segmentation tasks demonstrate that CanKD outperforms state-of-the-art feature and hybrid distillation methods. These experimental results highlight CanKD's potential as a new paradigm for attention-guided distillation in computer vision tasks. Code is available at https://github.com/tori-hotaru/CanKD
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21381v1" target="_blank" rel="noopener noreferrer">
                BanglaASTE：一种基于集成深度学习在孟加拉语电商评论中进行方面-情感-观点提取的新型框架
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            BanglaASTE: A Novel Framework for Aspect-Sentiment-Opinion Extraction in Bangla E-commerce Reviews Using Ensemble Deep Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ariful Islam, Md Rifat Hossen, Abir Ahmed, B M Taslimul Haque
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定语言（孟加拉语）的电商评论情感分析，属于领域特定的NLP应用。虽然电商评论与推荐系统有一定关联，但该工作主要关注情感分析而非核心推荐算法，且缺乏对LLM或Transformer架构的显著贡献。其集成深度学习方法在推荐系统中可能有有限的应用价值，用于改进商品评论的情感理解。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:27:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21381v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21381v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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            <details class="border-t border-gray-200 pt-4 mt-4">
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Aspect-Based Sentiment Analysis (ABSA) has emerged as a critical tool for extracting fine-grained sentiment insights from user-generated content, particularly in e-commerce and social media domains. However, research on Bangla ABSA remains significantly underexplored due to the absence of comprehensive datasets and specialized frameworks for triplet extraction in this language. This paper introduces BanglaASTE, a novel framework for Aspect Sentiment Triplet Extraction (ASTE) that simultaneously identifies aspect terms, opinion expressions, and sentiment polarities from Bangla product reviews. Our contributions include: (1) creation of the first annotated Bangla ASTE dataset containing 3,345 product reviews collected from major e-commerce platforms including Daraz, Facebook, and Rokomari; (2) development of a hybrid classification framework that employs graph-based aspect-opinion matching with semantic similarity techniques; and (3) implementation of an ensemble model combining BanglaBERT contextual embeddings with XGBoost boosting algorithms for enhanced triplet extraction performance. Experimental results demonstrate that our ensemble approach achieves superior performance with 89.9% accuracy and 89.1% F1-score, significantly outperforming baseline models across all evaluation metrics. The framework effectively addresses key challenges in Bangla text processing including informal expressions, spelling variations, and data sparsity. This research advances the state-of-the-art in low-resource language sentiment analysis and provides a scalable solution for Bangla e-commerce analytics applications.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21218v1" target="_blank" rel="noopener noreferrer">
                在少量人类样本上微调大语言模型能否增加异质性、对齐性和信念-行动一致性？
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Can Finetuing LLMs on Small Human Samples Increase Heterogeneity, Alignment, and Belief-Action Coherence?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Steven Wang, Kyle Hunt, Shaojie Tang, Kenneth Joseph
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM微调对人类对齐和异质性的影响，属于LLM对齐和伦理范畴。虽然涉及LLM技术，但其核心焦点是信念-行动一致性和人类价值观对齐，这些属于伦理对齐领域，与推荐系统、搜索或广告的技术核心进展相关性较弱。论文没有明确展示在RecSys/Search/Ads中的直接应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:50:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21218v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21218v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    There is ongoing debate about whether large language models (LLMs) can serve as substitutes for human participants in survey and experimental research. While recent work in fields such as marketing and psychology has explored the potential of LLM-based simulation, a growing body of evidence cautions against this practice: LLMs often fail to align with real human behavior, exhibiting limited diversity, systematic misalignment for minority subgroups, insufficient within-group variance, and discrepancies between stated beliefs and actions. This study examines an important and distinct question in this domain: whether fine-tuning on a small subset of human survey data, such as that obtainable from a pilot study, can mitigate these issues and yield realistic simulated outcomes. Using a behavioral experiment on information disclosure, we compare human and LLM-generated responses across multiple dimensions, including distributional divergence, subgroup alignment, belief-action coherence, and the recovery of regression coefficients. We find that fine-tuning on small human samples substantially improves heterogeneity, alignment, and belief-action coherence relative to the base model. However, even the best-performing fine-tuned models fail to reproduce the regression coefficients of the original study, suggesting that LLM-generated data remain unsuitable for replacing human participants in formal inferential analyses.
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            <a href="https://www.alphaxiv.org/abs/2511.21635v1" target="_blank" rel="noopener noreferrer">
                视觉Transformer中非单调缩放机制的机理研究
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            <i class="fa fa-star mr-1"></i>3/10
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            Mechanisms of Non-Monotonic Scaling in Vision Transformers
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anantha Padmanaban Krishna Kumar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究视觉Transformer的缩放行为机制，属于Transformer架构的效率分析范畴，与'Enabling Transformer Tech'相关。虽然视觉Transformer的研究可能为处理图像特征的推荐/搜索系统提供效率改进的启发，但论文标题未明确表明与推荐系统、搜索或广告的直接应用潜力，因此相关性有限。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:07:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21635v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21635v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                    Deeper Vision Transformers often perform worse than shallower ones, which challenges common scaling assumptions. Through a systematic empirical analysis of ViT-S, ViT-B, and ViT-L on ImageNet, we identify a consistent three-phase Cliff-Plateau-Climb pattern that governs how representations evolve with depth. We observe that better performance is associated with progressive marginalization of the [CLS] token, originally designed as a global aggregation hub, in favor of distributed consensus among patch tokens. We quantify patterns of information mixing with an Information Scrambling Index, and show that in ViT-L the information-task tradeoff emerges roughly 10 layers later than in ViT-B, and that these additional layers correlate with increased information diffusion rather than improved task performance. Taken together, these results suggest that transformer architectures in this regime may benefit more from carefully calibrated depth that executes clean phase transitions than from simply increasing parameter count. The Information Scrambling Index provides a useful diagnostic for existing models and suggests a potential design target for future architectures. All code is available at: https://github.com/AnanthaPadmanaban-KrishnaKumar/Cliff-Plateau-Climb.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21541v1" target="_blank" rel="noopener noreferrer">
                视频生成模型是优秀的潜在奖励模型
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
        </span>
    </div>
    
    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Video Generation Models Are Good Latent Reward Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoyue Mi, Wenqing Yu, Jiesong Lian, Shibo Jie, Ruizhe Zhong, Zijun Liu, Guozhe...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文探讨视频生成模型作为潜在奖励模型的应用，这属于核心LLM技术的进展。虽然视频生成本身与推荐/搜索/广告的直接相关性较弱，但将生成模型用作奖励模型的技术思路可能应用于推荐系统的强化学习优化或内容质量评估，因此给予较低但非零的分数。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:14:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21541v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21541v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Reward feedback learning (ReFL) has proven effective for aligning image generation with human preferences. However, its extension to video generation faces significant challenges. Existing video reward models rely on vision-language models designed for pixel-space inputs, confining ReFL optimization to near-complete denoising steps after computationally expensive VAE decoding. This pixel-space approach incurs substantial memory overhead and increased training time, and its late-stage optimization lacks early-stage supervision, refining only visual quality rather than fundamental motion dynamics and structural coherence. In this work, we show that pre-trained video generation models are naturally suited for reward modeling in the noisy latent space, as they are explicitly designed to process noisy latent representations at arbitrary timesteps and inherently preserve temporal information through their sequential modeling capabilities. Accordingly, we propose Process Reward Feedback Learning~(PRFL), a framework that conducts preference optimization entirely in latent space, enabling efficient gradient backpropagation throughout the full denoising chain without VAE decoding. Extensive experiments demonstrate that PRFL significantly improves alignment with human preferences, while achieving substantial reductions in memory consumption and training time compared to RGB ReFL.
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            <a href="https://www.alphaxiv.org/abs/2511.21477v1" target="_blank" rel="noopener noreferrer">
                面向高效视觉Transformer的频率感知令牌缩减
            </a>
        </h3>
        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Frequency-Aware Token Reduction for Efficient Vision Transformer
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dong-Jae Lee, Jiwan Hur, Jaehyun Choi, Jaemyung Yu, Junmo Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视觉Transformer的效率优化，属于Transformer架构效率改进范畴。虽然令牌缩减技术可能为处理长序列推荐或搜索场景提供效率启发，但论文明确针对视觉领域，与RecSys/Search/Ads的直接关联较弱。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:10:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21477v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21477v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision Transformers have demonstrated exceptional performance across various computer vision tasks, yet their quadratic computational complexity concerning token length remains a significant challenge. To address this, token reduction methods have been widely explored. However, existing approaches often overlook the frequency characteristics of self-attention, such as rank collapsing and over-smoothing phenomenon. In this paper, we propose a frequency-aware token reduction strategy that improves computational efficiency while preserving performance by mitigating rank collapsing. Our method partitions tokens into high-frequency tokens and low-frequency tokens. high-frequency tokens are selectively preserved, while low-frequency tokens are aggregated into a compact direct current token to retain essential low-frequency components. Through extensive experiments and analysis, we demonstrate that our approach significantly improves accuracy while reducing computational overhead and mitigating rank collapsing and over smoothing. Furthermore, we analyze the previous methods, shedding light on their implicit frequency characteristics and limitations.
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            <a href="https://www.alphaxiv.org/abs/2511.21428v1" target="_blank" rel="noopener noreferrer">
                从观察到行动：基于潜在动作的基元分割在工业场景下的视觉语言动作模型预训练
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiajie Zhang, Sören Schwertfeger, Alexander Kleiner
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言动作模型在工业环境中的预训练和动作分割，属于机器人学和具身AI领域。虽然提到了多模态建模，但其核心应用场景（工业机器人）与推荐系统、搜索或广告的直接关联性较弱。潜在的微弱关联可能在于多模态序列建模技术，但这种技术迁移的路径不够明确。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:19:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21428v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21428v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We present a novel unsupervised framework to unlock vast unlabeled human demonstration data from continuous industrial video streams for Vision-Language-Action (VLA) model pre-training. Our method first trains a lightweight motion tokenizer to encode motion dynamics, then employs an unsupervised action segmenter leveraging a novel "Latent Action Energy" metric to discover and segment semantically coherent action primitives. The pipeline outputs both segmented video clips and their corresponding latent action sequences, providing structured data directly suitable for VLA pre-training. Evaluations on public benchmarks and a proprietary electric motor assembly dataset demonstrate effective segmentation of key tasks performed by humans at workstations. Further clustering and quantitative assessment via a Vision-Language Model confirm the semantic coherence of the discovered action primitives. To our knowledge, this is the first fully automated end-to-end system for extracting and organizing VLA pre-training data from unstructured industrial videos, offering a scalable solution for embodied AI integration in manufacturing.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21415v1" target="_blank" rel="noopener noreferrer">
                DiverseVAR：平衡下一代视觉自回归模型的多样性与质量
            </a>
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        <div class="mb-2 text-base text-gray-700">
            DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Mingue Park, Prin Phunyaphibarn, Phillip Y. Lee, Minhyuk Sung
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉自回归模型的多样性与质量平衡，属于纯粹的视觉生成领域。虽然标题提到'下一代'模型，但缺乏与推荐系统、搜索或广告的直接关联说明。视觉自回归模型在广告创意生成等非排名任务中可能有应用，但这属于被排除的无关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:06:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21415v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21415v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiring retraining, fine-tuning, or substantial computational overhead. While VAR models have recently emerged as strong competitors to diffusion and flow models for image generation, they suffer from a critical limitation in diversity, often producing nearly identical images even for simple prompts. This issue has largely gone unnoticed amid the predominant focus on image quality. We address this limitation at test time in two stages. First, inspired by diversity enhancement techniques in diffusion models, we propose injecting noise into the text embedding. This introduces a trade-off between diversity and image quality: as diversity increases, the image quality sharply declines. To preserve quality, we propose scale-travel: a novel latent refinement technique inspired by time-travel strategies in diffusion models. Specifically, we use a multi-scale autoencoder to extract coarse-scale tokens that enable us to resume generation at intermediate stages. Extensive experiments show that combining text-embedding noise injection with our scale-travel refinement significantly enhances diversity while minimizing image-quality degradation, achieving a new Pareto frontier in the diversity-quality trade-off.
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            <a href="https://www.alphaxiv.org/abs/2511.21395v1" target="_blank" rel="noopener noreferrer">
                Monet：在超越图像和语言的潜在视觉空间中进行推理
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            Monet: Reasoning in Latent Visual Space Beyond Images and Language
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qixun Wang, Yang Shi, Yifei Wang, Yuanxing Zhang, Pengfei Wan, Kun Gai, Xianghua...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及视觉空间的潜在表示和推理，但与推荐系统、搜索或广告的直接相关性较弱。虽然潜在空间建模技术可能间接应用于用户行为序列的表示学习，但论文标题未明确表明在推荐系统、搜索或广告领域的应用潜力，且主要关注视觉模态而非文本或序列数据。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:46:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21395v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21395v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    "Thinking with images" has emerged as an effective paradigm for advancing visual reasoning, extending beyond text-only chains of thought by injecting visual evidence into intermediate reasoning steps. However, existing methods fall short of human-like abstract visual thinking, as their flexibility is fundamentally limited by external tools. In this work, we introduce Monet, a training framework that enables multimodal large language models (MLLMs) to reason directly within the latent visual space by generating continuous embeddings that function as intermediate visual thoughts. We identify two core challenges in training MLLMs for latent visual reasoning: high computational cost in latent-vision alignment and insufficient supervision over latent embeddings, and address them with a three-stage distillation-based supervised fine-tuning (SFT) pipeline. We further reveal a limitation of applying GRPO to latent reasoning: it primarily enhances text-based reasoning rather than latent reasoning. To overcome this, we propose VLPO (Visual-latent Policy Optimization), a reinforcement learning method that explicitly incorporates latent embeddings into policy gradient updates. To support SFT, we construct Monet-SFT-125K, a high-quality text-image interleaved CoT dataset containing 125K real-world, chart, OCR, and geometry CoTs. Our model, Monet-7B, shows consistent gains across real-world perception and reasoning benchmarks and exhibits strong out-of-distribution generalization on challenging abstract visual reasoning tasks. We also empirically analyze the role of each training component and discuss our early unsuccessful attempts, providing insights for future developments in visual latent reasoning. Our model, data, and code are available at https://github.com/NOVAglow646/Monet.
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            <a href="https://www.alphaxiv.org/abs/2511.21317v1" target="_blank" rel="noopener noreferrer">
                HTTM：面向更快视频图变换器的头级时序令牌合并
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            HTTM: Head-wise Temporal Token Merging for Faster VGGT
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Weitian Wang, Lukas Meiner, Rai Shubham, Cecilia De La Parra, Akash Kumar
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种头级时序令牌合并方法用于加速视频图变换器，这属于Transformer架构效率优化（Enabling Transformer Tech）。虽然视频处理本身不在核心关注范围，但高效的令牌合并技术可能应用于处理用户行为序列等时序数据，从而提升推荐或搜索系统中序列建模的效率。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:04:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21317v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21317v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    The Visual Geometry Grounded Transformer (VGGT) marks a significant leap forward in 3D scene reconstruction, as it is the first model that directly infers all key 3D attributes (camera poses, depths, and dense geometry) jointly in one pass. However, this joint inference mechanism requires global attention layers that perform all-to-all attention computation on tokens from all views. For reconstruction of large scenes with long-sequence inputs, this causes a significant latency bottleneck. In this paper, we propose head-wise temporal merging (HTTM), a training-free 3D token merging method for accelerating VGGT. Existing merging techniques merge tokens uniformly across different attention heads, resulting in identical tokens in the layers' output, which hinders the model's representational ability. HTTM tackles this problem by merging tokens in multi-head granularity, which preserves the uniqueness of feature tokens after head concatenation. Additionally, this enables HTTM to leverage the spatial locality and temporal correspondence observed at the head level to achieve higher merging ratios with lower merging costs compared to existing methods. Thus, HTTM achieves up to 7x acceleration with negligible performance drops in a GPU-based inference.
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            <a href="https://www.alphaxiv.org/abs/2511.21139v1" target="_blank" rel="noopener noreferrer">
                基于跨模态代理查询的参考视频对象分割
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Referring Video Object Segmentation with Cross-Modality Proxy Queries
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Baoli Sun, Xinzhu Ma, Ning Wang, Zhihui Wang, Zhiyong Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频对象分割的计算机视觉任务，虽然涉及跨模态（可能视觉-语言）交互，但其核心是分割而非推荐、搜索或广告应用。作为潜在的使能技术，跨模态查询机制可能启发推荐系统中处理异构用户行为序列和上下文特征的方法，但直接相关性有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:45:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21139v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21139v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Referring video object segmentation (RVOS) is an emerging cross-modality task that aims to generate pixel-level maps of the target objects referred by given textual expressions. The main concept involves learning an accurate alignment of visual elements and language expressions within a semantic space. Recent approaches address cross-modality alignment through conditional queries, tracking the target object using a query-response based mechanism built upon transformer structure. However, they exhibit two limitations: (1) these conditional queries lack inter-frame dependency and variation modeling, making accurate target tracking challenging amid significant frame-to-frame variations; and (2) they integrate textual constraints belatedly, which may cause the video features potentially focus on the non-referred objects. Therefore, we propose a novel RVOS architecture called ProxyFormer, which introduces a set of proxy queries to integrate visual and text semantics and facilitate the flow of semantics between them. By progressively updating and propagating proxy queries across multiple stages of video feature encoder, ProxyFormer ensures that the video features are focused on the object of interest. This dynamic evolution also enables the establishment of inter-frame dependencies, enhancing the accuracy and coherence of object tracking. To mitigate high computational costs, we decouple cross-modality interactions into temporal and spatial dimensions. Additionally, we design a Joint Semantic Consistency (JSC) training strategy to align semantic consensus between the proxy queries and the combined video-text pairs. Comprehensive experiments on four widely used RVOS benchmarks demonstrate the superiority of our ProxyFormer to the state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.21025v1" target="_blank" rel="noopener noreferrer">
                CaptionQA：您的图像描述是否与图像本身一样有用？
            </a>
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            <i class="fa fa-star mr-1"></i>3/10
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            CaptionQA: Is Your Caption as Useful as the Image Itself?
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shijia Yang, Yunong Liu, Bohan Zhai, Ximeng Sun, Zicheng Liu, Emad Barsoum, Manl...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注图像描述（caption）与图像本身在问答任务中的效用比较，属于视觉-语言交互的评估范畴。虽然标题暗示了多模态理解的概念，但与推荐系统、搜索或广告的核心技术关联较弱，缺乏明确的Transformer架构创新或直接应用于这些领域的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:43:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21025v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21025v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image captions serve as efficient surrogates for visual content in multimodal systems such as retrieval, recommendation, and multi-step agentic inference pipelines. Yet current evaluation practices miss a fundamental question: Can captions stand-in for images in real downstream tasks? We propose a utility-based benchmark, CaptionQA, to evaluate model-generated captions, where caption quality is measured by how well it supports downstream tasks. CaptionQA is an extensible domain-dependent benchmark covering 4 domains--Natural, Document, E-commerce, and Embodied AI--each with fine-grained taxonomies (25 top-level and 69 subcategories) that identify useful information for domain-specific tasks. CaptionQA builds 33,027 densely annotated multiple-choice questions (50.3 per image on average) that explicitly require visual information to answer, providing a comprehensive probe of caption utility. In our evaluation protocol, an LLM answers these questions using captions alone, directly measuring whether captions preserve image-level utility and are utilizable by a downstream LLM. Evaluating state-of-the-art MLLMs reveals substantial gaps between the image and its caption utility. Notably, models nearly identical on traditional image-QA benchmarks lower by up to 32% in caption utility. We release CaptionQA along with an open-source pipeline for extension to new domains. The code is available at https://github.com/bronyayang/CaptionQA.
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            <a href="https://www.alphaxiv.org/abs/2511.21021v1" target="_blank" rel="noopener noreferrer">
                结构感知原型引导的可信多视图分类
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            <i class="fa fa-star mr-1"></i>3/10
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        <div class="mb-2 text-base text-gray-700">
            Structure-Aware Prototype Guided Trusted Multi-View Classification
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haojian Huang, Jiahao Shi, Zhe Liu, Harold Haodong Chen, Han Fang, Hao Sun, Zhon...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文涉及多视图学习和可信AI，虽然多视图学习在推荐系统中处理异构用户数据方面有潜在应用，但论文标题强调分类任务和可信性，与排名、检索或广告的核心关注点关联较弱。可信AI属于被排除的伦理相关主题，且没有明确指向推荐系统、搜索或广告的具体应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:33:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21021v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21021v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Trustworthy multi-view classification (TMVC) addresses the challenge of achieving reliable decision-making in complex scenarios where multi-source information is heterogeneous, inconsistent, or even conflicting. Existing TMVC approaches predominantly rely on globally dense neighbor relationships to model intra-view dependencies, leading to high computational costs and an inability to directly ensure consistency across inter-view relationships. Furthermore, these methods typically aggregate evidence from different views through manually assigned weights, lacking guarantees that the learned multi-view neighbor structures are consistent within the class space, thus undermining the trustworthiness of classification outcomes. To overcome these limitations, we propose a novel TMVC framework that introduces prototypes to represent the neighbor structures of each view. By simplifying the learning of intra-view neighbor relations and enabling dynamic alignment of intra- and inter-view structure, our approach facilitates more efficient and consistent discovery of cross-view consensus. Extensive experiments on multiple public multi-view datasets demonstrate that our method achieves competitive downstream performance and robustness compared to prevalent TMVC methods.
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            <a href="https://www.alphaxiv.org/abs/2511.21002v1" target="_blank" rel="noopener noreferrer">
                知识补全视觉：面向新闻图像字幕生成的多模态实体感知检索增强生成框架
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        <div class="mb-2 text-base text-gray-700">
            Knowledge Completes the Vision: A Multimodal Entity-aware Retrieval-Augmented Generation Framework for News Image Captioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiaoxing You, Qiang Huang, Lingyu Li, Chi Zhang, Xiaopeng Liu, Min Zhang, Jun Yu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及多模态（视觉+语言）和检索增强生成技术，但其核心应用场景是新闻图像字幕生成，属于纯粹的视觉-语言任务。虽然检索增强生成技术本身具有潜在价值，但论文没有明确展示其在推荐系统、搜索或广告领域的直接应用潜力，主要聚焦于内容生成而非排名或检索优化。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:03:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21002v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21002v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    News image captioning aims to produce journalistically informative descriptions by combining visual content with contextual cues from associated articles. Despite recent advances, existing methods struggle with three key challenges: (1) incomplete information coverage, (2) weak cross-modal alignment, and (3) suboptimal visual-entity grounding. To address these issues, we introduce MERGE, the first Multimodal Entity-aware Retrieval-augmented GEneration framework for news image captioning. MERGE constructs an entity-centric multimodal knowledge base (EMKB) that integrates textual, visual, and structured knowledge, enabling enriched background retrieval. It improves cross-modal alignment through a multistage hypothesis-caption strategy and enhances visual-entity matching via dynamic retrieval guided by image content. Extensive experiments on GoodNews and NYTimes800k show that MERGE significantly outperforms state-of-the-art baselines, with CIDEr gains of +6.84 and +1.16 in caption quality, and F1-score improvements of +4.14 and +2.64 in named entity recognition. Notably, MERGE also generalizes well to the unseen Visual News dataset, achieving +20.17 in CIDEr and +6.22 in F1-score, demonstrating strong robustness and domain adaptability.
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            <a href="https://www.alphaxiv.org/abs/2511.21005v1" target="_blank" rel="noopener noreferrer">
                ICPO：基于内在置信度驱动的高效强化学习群体相对偏好优化
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            ICPO: Intrinsic Confidence-Driven Group Relative Preference Optimization for Efficient Reinforcement Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jinpeng Wang, Chao Li, Ting Ye, Mengyuan Zhang, Wei Liu, Jian Luan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注强化学习中的偏好优化方法，虽然标题提到高效强化学习，但未明确说明与推荐系统、搜索或广告的具体关联。强化学习本身在推荐系统中有应用，但该论文聚焦于内在置信度和群体相对偏好优化，这些技术细节缺乏明确的RecSys/Search/Ads应用场景说明，因此相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:10:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21005v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21005v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.IR</span></div>
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                    Reinforcement Learning with Verifiable Rewards (RLVR) demonstrates significant potential in enhancing the reasoning capabilities of Large Language Models (LLMs). However, existing RLVR methods are often constrained by issues such as coarse-grained rewards, reward noise, and inefficient exploration, which lead to unstable training and entropy collapse. To address this challenge, we propose the Intrinsic Confidence-Driven Group Relative Preference Optimization method (ICPO). The intuition behind it lies in the fact that the probabilities of an LLM generating different responses can inherently and directly reflect its self-assessment of the reasoning process. Inspired by the idea of preference modeling, ICPO calculates a preference advantage score for each response by comparing the relative generation probabilities of multiple responses under the same input prompt, and integrates this score with verifiable rewards to guide the exploration process. We have discovered that the preference advantage score not only alleviates the issues of coarse-grained rewards and reward noise but also effectively curbs overconfident errors, enhances the relative superiority of undervalued high-quality responses, and prevents the model from overfitting to specific strategies, thereby facilitating more thorough exploration. Comprehensive experiments across four general-domain benchmarks and three mathematical benchmarks demonstrate that ICPO steadily boosts reasoning compared to GRPO.
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            <a href="https://www.alphaxiv.org/abs/2511.21688v1" target="_blank" rel="noopener noreferrer">
                G²VLM：基于几何基础的视觉语言模型，实现统一的三维重建与空间推理
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            G$^2$VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Wenbo Hu, Jingli Lin, Yilin Long, Yunlong Ran, Lihan Jiang, Yifan Wang, Chenming...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型在3D重建和空间推理方面的应用，属于纯粹的视觉领域研究。虽然标题中提到'统一建模'概念，但其核心内容围绕3D几何和视觉空间处理，与推荐系统、搜索或广告中的异构数据处理没有直接关联。这种3D视觉技术在当前阶段难以转化为推荐、搜索或广告领域的实际应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21688v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21688v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language Models (VLMs) still lack robustness in spatial intelligence, demonstrating poor performance on spatial understanding and reasoning tasks. We attribute this gap to the absence of a visual geometry learning process capable of reconstructing 3D space from 2D images. We present G$^2$VLM, a geometry grounded vision-language model that bridges two fundamental aspects of spatial intelligence: spatial 3D reconstruction and spatial understanding. G$^2$VLM natively leverages learned 3D visual geometry features to directly predict 3D attributes and enhance spatial reasoning tasks via in-context learning and interleaved reasoning. Our unified design is highly scalable for spatial understanding: it trains on abundant multi-view image and video data, while simultaneously leveraging the benefits of 3D visual priors that are typically only derived from hard-to-collect annotations. Experimental results demonstrate G$^2$VLM is proficient in both tasks, achieving comparable results to state-of-the-art feed-forward 3D reconstruction models and achieving better or competitive results across spatial understanding and reasoning tasks. By unifying a semantically strong VLM with low-level 3D vision tasks, we hope G$^2$VLM can serve as a strong baseline for the community and unlock more future applications, such as 3D scene editing.
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            <a href="https://www.alphaxiv.org/abs/2511.21686v1" target="_blank" rel="noopener noreferrer">
                Matrix：点对点多智能体合成数据生成框架
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            Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dong Wang, Yang Li, Ansong Ni, Ching-Feng Yeh, Youssef Emad, Xinjie Lei, Liam Ro...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然合成数据生成在数据增强方面有潜在价值，但该论文主要关注多智能体系统的点对点框架，这与推荐系统、搜索或广告的核心技术关联较弱。论文标题没有明确表明该框架在推荐、搜索或广告领域的直接应用潜力，也没有涉及LLM、Transformer架构或异构数据建模等关键技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:28
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21686v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21686v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Synthetic data has become increasingly important for training large language models, especially when real data is scarce, expensive, or privacy-sensitive. Many such generation tasks require coordinated multi-agent workflows, where specialized agents collaborate to produce data that is higher quality, more diverse, and structurally richer. However, existing frameworks for multi-agent synthesis often depend on a centralized orchestrator, creating scalability bottlenecks, or are hardcoded for specific domains, limiting flexibility. We present \textbf{Matrix}, a decentralized framework that represents both control and data flow as serialized messages passed through distributed queues. This peer-to-peer design eliminates the central orchestrator. Each task progresses independently through lightweight agents, while compute-intensive operations, such as LLM inference or containerized environments, are handled by distributed services. Built on Ray, Matrix scales to tens of thousands of concurrent agentic workflows and provides a modular, configurable design that enables easy adaptation to a wide range of data generation workflows. We evaluate Matrix across diverse synthesis scenarios, such as multi-agent collaborative dialogue, web-based reasoning data extraction, and tool-use trajectory generation in customer service environments. In all cases, Matrix achieves $2$--$15\times$ higher data generation throughput under identical hardware resources, without compromising output quality.
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            <a href="https://www.alphaxiv.org/abs/2511.21624v1" target="_blank" rel="noopener noreferrer">
                TAGFN：一个用于大语言模型时代假新闻检测的文本属性图数据集
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            TAGFN: A Text-Attributed Graph Dataset for Fake News Detection in the Age of LLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kay Liu, Yuwei Han, Haoyan Xu, Henry Peng Zou, Yue Zhao, Philip S. Yu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及文本和图数据，这表面上与处理异构数据相关，但其核心应用是假新闻检测，这属于内容安全领域而非推荐系统、搜索或广告的核心排名问题。假新闻检测主要关注内容可信度验证，与用户偏好建模、个性化推荐或广告相关性排序没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:49:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21624v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21624v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.SI</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large Language Models (LLMs) have recently revolutionized machine learning on text-attributed graphs, but the application of LLMs to graph outlier detection, particularly in the context of fake news detection, remains significantly underexplored. One of the key challenges is the scarcity of large-scale, realistic, and well-annotated datasets that can serve as reliable benchmarks for outlier detection. To bridge this gap, we introduce TAGFN, a large-scale, real-world text-attributed graph dataset for outlier detection, specifically fake news detection. TAGFN enables rigorous evaluation of both traditional and LLM-based graph outlier detection methods. Furthermore, it facilitates the development of misinformation detection capabilities in LLMs through fine-tuning. We anticipate that TAGFN will be a valuable resource for the community, fostering progress in robust graph-based outlier detection and trustworthy AI. The dataset is publicly available at https://huggingface.co/datasets/kayzliu/TAGFN and our code is available at https://github.com/kayzliu/tagfn.
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            <a href="https://www.alphaxiv.org/abs/2511.21568v1" target="_blank" rel="noopener noreferrer">
                RoParQ：面向释义问题鲁棒性的大语言模型释义感知对齐
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            RoParQ: Paraphrase-Aware Alignment of Large Language Models Towards Robustness to Paraphrased Questions
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Minjoon Choi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注大语言模型对释义问题的鲁棒性改进，这属于NLP领域的模型鲁棒性和泛化能力研究。虽然涉及LLM技术，但其核心应用场景是问答系统的鲁棒性，与推荐系统、搜索或广告的排名优化没有直接关联，且没有明确展示在RecSys/Search/Ads领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:40:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21568v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21568v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large Language Models (LLMs) often exhibit inconsistent behavior when answering paraphrased questions, suggesting a reliance on surface-level patterns rather than true semantic understanding. To address this limitation, we introduce RoParQ, a benchmark specifically constructed to evaluate cross-paraphrase consistency in closed-book multiple-choice QA. This benchmark is derived from standard datasets by generating paraphrases via proprietary models and selectively retaining examples that elicit inconsistent confidence from a judge model. We further propose XParaCon, a novel evaluation metric that quantifies a model's robustness by measuring the standard deviation of accuracies across question variants. Additionally, we implement a reasoning-based, paraphrase-aware Supervised Fine-Tuning (SFT) strategy designed to align models toward semantic invariance. Our experiments demonstrate that this targeted alignment significantly enhances robustness. Notably, fine-tuned lightweight models achieved consistency levels comparable to much larger pre-trained models. These results highlight the efficacy of our approach in mitigating superficial memorization and fostering more robust, reliable LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.21517v1" target="_blank" rel="noopener noreferrer">
                语音、偏见与共指：语音翻译中性别可解释性研究
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            Voice, Bias, and Coreference: An Interpretability Study of Gender in Speech Translation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lina Conti, Dennis Fucci, Marco Gaido, Matteo Negri, Guillaume Wisniewski, Luisa...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语音翻译中的性别偏见和可解释性问题，属于语音处理和NLP偏见研究的交叉领域。虽然涉及可解释性，但其核心关注语音翻译中的性别偏见，与推荐系统、搜索或广告的核心技术进展缺乏直接关联。语音翻译本身不属于当前关注的LLM在RecSys/Search/Ads中的直接应用范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:48:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21517v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21517v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Unlike text, speech conveys information about the speaker, such as gender, through acoustic cues like pitch. This gives rise to modality-specific bias concerns. For example, in speech translation (ST), when translating from languages with notional gender, such as English, into languages where gender-ambiguous terms referring to the speaker are assigned grammatical gender, the speaker's vocal characteristics may play a role in gender assignment. This risks misgendering speakers, whether through masculine defaults or vocal-based assumptions. Yet, how ST models make these decisions remains poorly understood. We investigate the mechanisms ST models use to assign gender to speaker-referring terms across three language pairs (en-es/fr/it), examining how training data patterns, internal language model (ILM) biases, and acoustic information interact. We find that models do not simply replicate term-specific gender associations from training data, but learn broader patterns of masculine prevalence. While the ILM exhibits strong masculine bias, models can override these preferences based on acoustic input. Using contrastive feature attribution on spectrograms, we reveal that the model with higher gender accuracy relies on a previously unknown mechanism: using first-person pronouns to link gendered terms back to the speaker, accessing gender information distributed across the frequency spectrum rather than concentrated in pitch.
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            <a href="https://www.alphaxiv.org/abs/2511.21473v1" target="_blank" rel="noopener noreferrer">
                用于长文档可读性评估的层次化排序神经网络
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        <div class="mb-2 text-base text-gray-700">
            Hierarchical Ranking Neural Network for Long Document Readability Assessment
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yurui Zheng, Yijun Chen, Shaohong Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文档可读性评估，这是一个与内容理解和质量评估相关的NLP任务。虽然排序神经网络技术可能在某些搜索场景中有间接应用，但该工作主要针对文档理解而非推荐系统、搜索或广告的核心排名问题，与当前关注的领域直接相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:05:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21473v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21473v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Readability assessment aims to evaluate the reading difficulty of a text. In recent years, while deep learning technology has been gradually applied to readability assessment, most approaches fail to consider either the length of the text or the ordinal relationship of readability labels. This paper proposes a bidirectional readability assessment mechanism that captures contextual information to identify regions with rich semantic information in the text, thereby predicting the readability level of individual sentences. These sentence-level labels are then used to assist in predicting the overall readability level of the document. Additionally, a pairwise sorting algorithm is introduced to model the ordinal relationship between readability levels through label subtraction. Experimental results on Chinese and English datasets demonstrate that the proposed model achieves competitive performance and outperforms other baseline models.
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            <a href="https://www.alphaxiv.org/abs/2511.21402v1" target="_blank" rel="noopener noreferrer">
                文本到SQL作为双状态推理：集成自适应上下文与渐进式生成
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            <i class="fa fa-star mr-1"></i>2/10
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            Text-to-SQL as Dual-State Reasoning: Integrating Adaptive Context and Progressive Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhifeng Hao, Qibin Song, Ruichu Cai, Boyan Xu
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注文本到SQL转换任务，这是自然语言处理中的特定领域应用。虽然涉及上下文建模和渐进生成技术，但这些方法在推荐系统、搜索或广告中的潜在应用非常有限且间接。该工作本质上是数据库查询优化而非核心推荐/搜索技术。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:52:50
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21402v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21402v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Recent divide-and-conquer reasoning approaches, particularly those based on Chain-of-Thought (CoT), have substantially improved the Text-to-SQL capabilities of Large Language Models (LLMs). However, when applied to complex enterprise databases, such methods struggle to maintain coherent reasoning due to limited context capacity, unreliable schema linking, and weak grounding in database semantics. To overcome these issues, we introduce DSR-SQL, a \textbf{D}ual-\textbf{S}tate \textbf{R}easoning framework that models Text-to-SQL as an interaction between an adaptive context state and a progressive generation state. The first constructs a compact, semantically faithful environment by refining large schemas and selecting relevant structures, while the second formalizes SQL synthesis as feedback-guided state transitions, enabling the model to self-correct and align with user intent. Without any post-training or in-context examples, DSR-SQL achieves competitive performance, reaching 35.28\% execution accuracy on Spider 2.0-Snow and 68.32\% on BIRD development set. Our implementation will be open-sourced at: https://github.com/DMIRLAB-Group/DSR-SQL.
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            <a href="https://www.alphaxiv.org/abs/2511.21401v1" target="_blank" rel="noopener noreferrer">
                大型语言模型能否为基于证据的事实核查提取类人细粒度证据？
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            Can LLMs extract human-like fine-grained evidence for evidence-based fact-checking?
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Antonín Jarolím, Martin Fajčík, Lucia Makaiová
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLMs在事实核查和证据提取方面的能力，这属于纯粹的NLP评估任务，与推荐系统、搜索或广告的核心技术无关。虽然涉及LLMs，但缺乏在RecSys/Search/Ads领域的潜在应用场景，属于被排除的'纯粹NLP-centric topics'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:51:59
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                <a href="https://arxiv.org/abs/2511.21401v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21401v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Misinformation frequently spreads in user comments under online news articles, highlighting the need for effective methods to detect factually incorrect information. To strongly support or refute claims extracted from such comments, it is necessary to identify relevant documents and pinpoint the exact text spans that justify or contradict each claim. This paper focuses on the latter task -- fine-grained evidence extraction for Czech and Slovak claims. We create new dataset, containing two-way annotated fine-grained evidence created by paid annotators. We evaluate large language models (LLMs) on this dataset to assess their alignment with human annotations. The results reveal that LLMs often fail to copy evidence verbatim from the source text, leading to invalid outputs. Error-rate analysis shows that the {llama3.1:8b model achieves a high proportion of correct outputs despite its relatively small size, while the gpt-oss-120b model underperforms despite having many more parameters. Furthermore, the models qwen3:14b, deepseek-r1:32b, and gpt-oss:20b demonstrate an effective balance between model size and alignment with human annotations.
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            <a href="https://www.alphaxiv.org/abs/2511.21397v1" target="_blank" rel="noopener noreferrer">
                推理型视觉语言模型在测试时计算量上是否呈反向缩放？一项基于干扰项中心的实证分析
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            Do Reasoning Vision-Language Models Inversely Scale in Test-Time Compute? A Distractor-centric Empirical Analysis
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiyun Bae, Hyunjong Ok, Sangwoo Mo, Jaeho Lee
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉语言模型的推理缩放特性，属于VLM评估范畴，与推荐系统、搜索或广告的直接应用关联较弱。虽然标题提及VLM，但焦点在于测试时计算效率的实证分析，而非将异构数据作为不同模态进行统一建模的创新方法，对当前关注领域的实际价值有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:49:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21397v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21397v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    How does irrelevant information (i.e., distractors) affect test-time scaling in vision-language models (VLMs)? Prior studies on language models have reported an inverse scaling effect, where textual distractors lead to longer but less effective reasoning. To investigate whether similar phenomena occur in multimodal settings, we introduce Idis (Images with distractors), a visual question-answering dataset that systematically varies distractors along semantic, numerical, and spatial dimensions. Our analyses reveal that visual distractors differ fundamentally from textual ones: although inverse scaling persists, adding visual distractors reduces accuracy without increasing reasoning length. We further show that tracking attribute counts within reasoning traces provides key insights into how distractors, reasoning length, and accuracy interact. Finally, we demonstrate that these trends extend to established visual bias benchmarks such as Waterbirds, and we propose a simple prompting strategy to mitigate bias-driven predictions in reasoning models.
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            <a href="https://www.alphaxiv.org/abs/2511.21334v1" target="_blank" rel="noopener noreferrer">
                神经语言模型中的涌现词汇语义学：在LLM生成文本上验证马丁定律
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            Emergent Lexical Semantics in Neural Language Models: Testing Martin's Law on LLM-Generated Text
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kai Kugler
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究LLM生成文本中的词汇语义涌现现象，属于语言模型内在机制分析。虽然涉及LLM技术，但研究焦点是语言学理论验证（马丁定律），缺乏明确的推荐系统、搜索或广告应用潜力。这更像是纯粹的NLP语言学研究，而非能够直接应用于工业场景的使能技术。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:31:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21334v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21334v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    We present the first systematic investigation of Martin's Law - the empirical relationship between word frequency and polysemy - in text generated by neural language models during training. Using DBSCAN clustering of contextualized embeddings as an operationalization of word senses, we analyze four Pythia models (70M-1B parameters) across 30 training checkpoints. Our results reveal a non-monotonic developmental trajectory: Martin's Law emerges around checkpoint 100, reaches peak correlation (r > 0.6) at checkpoint 104, then degrades by checkpoint 105. Smaller models (70M, 160M) experience catastrophic semantic collapse at late checkpoints, while larger models (410M, 1B) show graceful degradation. The frequency-specificity trade-off remains stable (r $\approx$ -0.3) across all models. These findings suggest that compliance with linguistic regularities in LLM-generated text is not monotonically increasing with training, but instead follows a balanced trajectory with an optimal semantic window. This work establishes a novel methodology for evaluating emergent linguistic structure in neural language models.
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            <a href="https://www.alphaxiv.org/abs/2511.21214v1" target="_blank" rel="noopener noreferrer">
                自引导防御：通过合成指导原则实现推理模型的自适应安全对齐
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            Self-Guided Defense: Adaptive Safety Alignment for Reasoning Models via Synthesized Guidelines
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhang Wang, Yanxu Zhu, Dongyuan Lu, Jitao Sang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM的安全对齐和防御机制，属于安全性和伦理对齐范畴，这在当前关注点中被明确列为不相关主题。虽然提到了推理模型，但核心焦点是安全防御而非推荐系统、搜索或广告的技术应用。该工作缺乏在推荐、搜索或广告领域的直接技术应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:44:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21214v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21214v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    Reasoning models have demonstrated remarkable capabilities in complex reasoning tasks. However, ensuring their safety against adversarial jailbreak prompts remains a critical challenge. Due to the covert and deceptive nature of such prompts, they can often evade built-in safety mechanisms and lead to the generation of harmful content. This underscores the need for an adaptive safety alignment approach that enables models to autonomously reinforce their defenses in response to adversarial inputs. This paper introduces the Synthesized Guideline-based Adaptive Safety Alignment (SGASA) framework, which internalizes model-generated safety guidelines to strengthen models' ability to enhance robustness against harmful adversarial prompts while minimizing unnecessary refusals of benign requests. SGASA consists of two key stages: Data Pre-synthesis, which generates safety guidelines and augmented prompts; and Alignment Fine-tuning, which leverages Supervised Fine-tuning (SFT) and Direct Preference Optimization (DPO) to embed these guidelines into the model. Extensive experiments across multiple datasets demonstrate that SGASA significantly improves model safety, validating its adaptive and scalable effectiveness.
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            <a href="https://www.alphaxiv.org/abs/2511.21101v1" target="_blank" rel="noopener noreferrer">
                MortgageLLM：基于残差指令迁移、对齐调优和任务特定路由的领域自适应预训练
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            MortgageLLM: Domain-Adaptive Pretraining with Residual Instruction Transfer, Alignment Tuning, and Task-Specific Routing
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Manish Jain, Satheesh Kumar Ponnambalam, Salman Faroz, Chandrakanth Lns, Vinay S...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然该论文涉及领域自适应预训练和指令调优等LLM技术，但其特定应用于抵押贷款领域，这属于金融领域的专业应用，与搜索、推荐或广告系统没有直接关联。论文标题中提到的技术（残差指令迁移、任务特定路由）可能具有通用性，但缺乏明确的RecSys/Search/Ads应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:37:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21101v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21101v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    Large Language Models (LLMs) demonstrate exceptional capabilities across general domains, yet their application to specialized sectors such as mortgage finance requires domain-specific knowledge augmentation while preserving instruction-following fidelity. We present MortgageLLM, a novel domain-specific large language model that addresses this dual challenge. It is developed using a dual-track specialization framework from a single base model (LLaMA-3.1-8B). We opted for this dual-expert approach as a single multi-task model suffers from performance trade-offs, where optimizing for structured tasks (via SFT) degrades conversational fidelity (via DPO). Our dual-track method solves this by creating two specialists, allowing each to be optimally trained for its distinct capability. Our approach applies the instruction residual technique to restore instruction-following capabilities post-domain adaptation without supervised fine-tuning. We contribute: (1) application of this residual technique to the highly specialized mortgage finance domain; (2) a dual-expert architecture combining a conversational Q&A model and a structured task model for classification and summarization; and (3) an intelligent task routing mechanism using few-shot classification performed by one of the expert models itself. We validate our approach on domain-specific benchmarks, where our final model (MLM v2) significantly outperforms the base LLaMA-3.1-8B-Instruct, achieving an LLM-as-a-Judge summarization score of 4.58 (vs. 3.99), a Q&A score of 4.09 (vs. 4.0), and a classification score of 2.6 (vs. 1.2). On semantic similarity, our model achieved a BERTScore of 0.77 for summarization (vs. 0.74), 0.68 for Q&A (vs. 0.58), and 0.75 for classification (vs. 0.73), substantially outperforming baseline approaches.
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            <a href="https://www.alphaxiv.org/abs/2511.21088v1" target="_blank" rel="noopener noreferrer">
                基于语音特征的增强对齐Transformer在低资源缅甸语中的ASR错误纠正
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            ASR Error Correction in Low-Resource Burmese with Alignment-Enhanced Transformers using Phonetic Features
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ye Bhone Lin, Thura Aung, Ye Kyaw Thu, Thazin Myint Oo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注语音识别错误纠正，属于语音处理领域，与搜索、推荐或广告系统的核心焦点相关性较弱。虽然使用了Transformer架构，但应用场景局限于特定语言的语音处理，缺乏明确的RecSys/Search/Ads应用潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:13:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21088v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21088v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span><span class="category-tag">cs.SD</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper investigates sequence-to-sequence Transformer models for automatic speech recognition (ASR) error correction in low-resource Burmese, focusing on different feature integration strategies including IPA and alignment information. To our knowledge, this is the first study addressing ASR error correction specifically for Burmese. We evaluate five ASR backbones and show that our ASR Error Correction (AEC) approaches consistently improve word- and character-level accuracy over baseline outputs. The proposed AEC model, combining IPA and alignment features, reduced the average WER of ASR models from 51.56 to 39.82 before augmentation (and 51.56 to 43.59 after augmentation) and improving chrF++ scores from 0.5864 to 0.627, demonstrating consistent gains over the baseline ASR outputs without AEC. Our results highlight the robustness of AEC and the importance of feature design for improving ASR outputs in low-resource settings.
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            <a href="https://www.alphaxiv.org/abs/2511.21086v1" target="_blank" rel="noopener noreferrer">
                大型语言模型中的正交约束满足与人类困难对齐
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Orthographic Constraint Satisfaction and Human Difficulty Alignment in Large Language Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Bryan E. Tuck, Rakesh M. Verma
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究语言模型在拼写约束满足方面与人类认知困难的对齐问题，这属于语言模型行为分析和认知对齐的范畴。虽然涉及LLM技术，但该研究聚焦于拼写约束和人类认知对齐，与推荐系统、搜索或广告的核心技术需求缺乏直接关联。这种约束满足研究主要服务于模型理解和认知科学，在RecSys/Search/Ads领域的实际应用潜力有限。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:12:33
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21086v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21086v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large language models must satisfy hard orthographic constraints during controlled text generation, yet systematic cross-architecture evaluation remains limited. We evaluate 28 configurations spanning three model families (Qwen3, Claude Haiku-4.5, GPT-5-mini) on 58 word puzzles requiring character-level constraint satisfaction. Architectural differences produce substantially larger performance gaps (2.0-2.2x, F1=0.761 vs. 0.343) than parameter scaling within families (83% gain from eightfold scaling), suggesting that constraint satisfaction may require specialized architectural features or training objectives beyond standard language model scaling. Thinking budget sensitivity proves heterogeneous: high-capacity models show strong returns (+0.102 to +0.136 F1), while mid-sized variants saturate or degrade. These patterns are inconsistent with uniform compute benefits. Using difficulty ratings from 10,000 human solvers per puzzle, we establish modest but consistent calibration (r=0.24-0.38) across all families, yet identify systematic failures on common words with unusual orthography ("data", "poop", "loll": 86-95% human success, 89-96% model miss rate). These failures reveal over-reliance on distributional plausibility that penalizes orthographically atypical but constraint-valid patterns, suggesting architectural innovations may be required beyond simply scaling parameters or computational budgets.
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            <a href="https://www.alphaxiv.org/abs/2511.21081v1" target="_blank" rel="noopener noreferrer">
                使用科尔莫戈罗夫-阿诺德网络头微调增强缅甸新闻分类
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            Enhancing Burmese News Classification with Kolmogorov-Arnold Network Head Fine-tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Thura Aung, Eaint Kay Khaing Kyaw, Ye Kyaw Thu, Thazin Myint Oo, Thepchai Supnit...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注特定语言（缅甸语）的新闻分类任务，这属于领域特定的NLP应用。虽然提到了Kolmogorov-Arnold网络这一新兴架构，但其应用场景（新闻分类）与推荐系统、搜索或广告的核心技术关联性较弱，且没有明确展示在异构数据建模或推荐相关任务中的潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 05:50:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21081v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21081v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    In low-resource languages like Burmese, classification tasks often fine-tune only the final classification layer, keeping pre-trained encoder weights frozen. While Multi-Layer Perceptrons (MLPs) are commonly used, their fixed non-linearity can limit expressiveness and increase computational cost. This work explores Kolmogorov-Arnold Networks (KANs) as alternative classification heads, evaluating Fourier-based FourierKAN, Spline-based EfficientKAN, and Grid-based FasterKAN-across diverse embeddings including TF-IDF, fastText, and multilingual transformers (mBERT, Distil-mBERT). Experimental results show that KAN-based heads are competitive with or superior to MLPs. EfficientKAN with fastText achieved the highest F1-score (0.928), while FasterKAN offered the best trade-off between speed and accuracy. On transformer embeddings, EfficientKAN matched or slightly outperformed MLPs with mBERT (0.917 F1). These findings highlight KANs as expressive, efficient alternatives to MLPs for low-resource language classification.
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            <a href="https://www.alphaxiv.org/abs/2511.21066v1" target="_blank" rel="noopener noreferrer">
                面向讽刺检测的上下文感知实用元认知提示方法
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            Context-Aware Pragmatic Metacognitive Prompting for Sarcasm Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Michael Iskandardinata, William Christian, Derwin Suhartono
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于讽刺检测这一特定NLP任务，属于纯粹的语言理解问题，与推荐系统、搜索或广告的核心技术需求关联度极低。虽然提到了上下文感知和提示方法，但这些技术在该论文中的应用局限于讽刺识别，无法直接转化为推荐、搜索或广告领域的实际应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 05:19:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21066v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21066v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Detecting sarcasm remains a challenging task in the areas of Natural Language Processing (NLP) despite recent advances in neural network approaches. Currently, Pre-trained Language Models (PLMs) and Large Language Models (LLMs) are the preferred approach for sarcasm detection. However, the complexity of sarcastic text, combined with linguistic diversity and cultural variation across communities, has made the task more difficult even for PLMs and LLMs. Beyond that, those models also exhibit unreliable detection of words or tokens that require extra grounding for analysis. Building on a state-of-the-art prompting method in LLMs for sarcasm detection called Pragmatic Metacognitive Prompting (PMP), we introduce a retrieval-aware approach that incorporates retrieved contextual information for each target text. Our pipeline explores two complementary ways to provide context: adding non-parametric knowledge using web-based retrieval when the model lacks necessary background, and eliciting the model's own internal knowledge for a self-knowledge awareness strategy. We evaluated our approach with three datasets, such as Twitter Indonesia Sarcastic, SemEval-2018 Task 3, and MUStARD. Non-parametric retrieval resulted in a significant 9.87% macro-F1 improvement on Twitter Indonesia Sarcastic compared to the original PMP method. Self-knowledge retrieval improves macro-F1 by 3.29% on Semeval and by 4.08% on MUStARD. These findings highlight the importance of context in enhancing LLMs performance in sarcasm detection task, particularly the involvement of culturally specific slang, references, or unknown terms to the LLMs. Future work will focus on optimizing the retrieval of relevant contextual information and examining how retrieval quality affects performance. The experiment code is available at: https://github.com/wllchrst/sarcasm-detection_pmp_knowledge-base.
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            <a href="https://www.alphaxiv.org/abs/2511.21016v1" target="_blank" rel="noopener noreferrer">
                门控卡尔曼网络：通过测试时岭回归实现的衰减记忆层
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            Gated KalmaNet: A Fading Memory Layer Through Test-Time Ridge Regression
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Liangzu Peng, Aditya Chattopadhyay, Luca Zancato, Elvis Nunez, Wei Xia, Stefano ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文提出了一种新的神经网络层架构，关注记忆机制和测试时优化，这属于通用的深度学习架构改进。虽然记忆机制在推荐系统中对用户行为序列建模有潜在价值，但论文标题没有明确指向推荐系统、搜索或广告的具体应用场景，也没有明确与Transformer架构或LLM技术相关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:26:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21016v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21016v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    As efficient alternatives to softmax Attention, linear state-space models (SSMs) achieve constant memory and linear compute, but maintain only a lossy, fading summary of the past, often leading to inferior performance in recall oriented tasks. We propose Gated KalmaNet (GKA), a layer that reduces this gap by accounting for the full past when predicting the next token, while maintaining SSM-style efficiency. GKA achieves this by solving an online ridge regression problem at test time, with constant memory and linear compute cost in the sequence length. Drawing inspiration from the Kalman Filter, we iteratively solve the online ridge regression problem. However, a critical insight is that standard Kalman filter equations are numerically unstable in low-precision environments (like bfloat16) and difficult to parallelize in modern hardware. We address both challenges through two key innovations: (1) an adaptive regularization strategy with input-dependent gating that controls the condition number of the ridge regression, ensuring numerical stability while balancing memory retention. And (2) the use of Chebyshev Iteration instead of other conventional iterative solvers, which we demonstrate to be more stable in low-precision settings. To further improve scalability, we develop a hardware-aware chunk-wise implementation of Chebyshev Iteration along with custom kernels for backpropagating through our adaptive regularization and gating mechanisms. Empirically, GKA shows strong language understanding capabilites on short-context tasks outperforming existing SSM layers (like Mamba2, GLA and Gated DeltaNet). On long-context, GKA excels at real-world RAG and LongQA tasks up to 128k tokens, achieving more than $10$% relative improvement over other fading memory baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.21006v1" target="_blank" rel="noopener noreferrer">
                TrackList：为开放大语言模型中的头部和尾部知识追溯查询语言多样性
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            TrackList: Tracing Back Query Linguistic Diversity for Head and Tail Knowledge in Open Large Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ioana Buhnila, Aman Sinha, Mathieu Constant
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注LLM中的知识分布和查询多样性分析，属于纯LLM评估范畴。虽然涉及头部/尾部知识概念，但缺乏明确的推荐系统、搜索或广告应用场景。论文焦点是模型内部知识分析而非实际应用，与当前关注的技术应用方向相关性较弱。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:14:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21006v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21006v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large Language Models (LLMs) have proven efficient in giving definition-type answers to user input queries. While for humans giving various types of answers, such as examples and paraphrases, is an easy task, LLMs struggle to provide correct answers for other than definition-type queries. In this study, we evaluated this drop in performance using TrackList, a fine-grained linguistic and statistical analysis pipeline to investigate the impact of the pre-training data on LLMs answers to diverse linguistic queries. We also introduce RefoMed-EN, an English dataset consisting of 6170 human-annotated medical terms alongside their corresponding definitions, denominations, exemplifications, explanations, or paraphrases. We studied whether the high frequency of a concept (head) or low frequency (tail) impacts the language model's performance. We evaluated the quality of the LLM's output using syntactic and semantic similarity metrics, statistical correlations and embeddings. Results showed that the LLM's task performance for definition type questions is the highest, while for the exemplification type it is the lowest. Additionally, we showed that for definition-type questions, large language models are prone to paraphrase more on popular and frequent knowledge and less on tail and technical knowledge, especially in the expert texts.
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            <a href="https://www.alphaxiv.org/abs/2511.20974v1" target="_blank" rel="noopener noreferrer">
                RosettaSpeech：基于单语数据的零样本语音到语音翻译
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            RosettaSpeech: Zero-Shot Speech-to-Speech Translation from Monolingual Data
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhisheng Zheng, Xiaohang Sun, Tuan Dinh, Abhishek Yanamandra, Abhinav Jain, Zhu ...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1"></p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:02:20
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20974v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20974v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.LG</span></div>
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                    The scarcity of parallel speech corpora critically hampers speech-to-speech translation (S2ST), often forcing reliance on complex, multi-stage pipelines. This paper introduces RosettaSpeech, a novel and simplified framework for zero-shot S2ST that is trained on monolingual speech-text data augmented by machine translation supervision. While our method leverages the linguistic knowledge inherent in text-based NMT models, it strictly eliminates the need for parallel speech-to-speech pairs. Our model uniquely uses text as an intermediate bridge during training but functions as a direct, end-to-end speech-to-speech model at inference. This streamlined approach achieves state-of-the-art results on standard benchmarks. For instance, on the CVSS-C test set, RosettaSpeech outperforms leading systems, achieving an ASR-BLEU score of 25.17 for German-to-English and 29.86 for Spanish-to-English-relative gains of over 27% and 14%, respectively. Furthermore, we demonstrate that a single model can deliver strong many-to-one translation performance (FR/ES/DE -> EN). We also provide a foundational analysis of how training data scaling impacts model performance. By prioritizing reliance on abundant parallel text rather than difficult-to-acquire parallel speech, RosettaSpeech offers a scalable path to creating high-quality, speaker-preserving S2ST for a much broader array of languages.
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            <a href="https://www.alphaxiv.org/abs/2511.20973v1" target="_blank" rel="noopener noreferrer">
                面向大型音频语言模型的音频令牌压缩研究
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            Towards Audio Token Compression in Large Audio Language Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Saurabhchand Bhati, Samuel Thomas, Hilde Kuehne, Rogerio Feris, James Glass
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频语言模型的令牌压缩技术，这属于音频领域的特定优化。虽然压缩技术本身可能有通用价值，但论文明确聚焦于音频模态，与推荐系统、搜索或广告中的文本/序列数据处理缺乏直接关联。音频令牌压缩在推荐/搜索/广告领域的潜在应用场景非常有限且不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:00:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20973v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20973v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.AS</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span></div>
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                    Large Audio Language Models (LALMs) demonstrate impressive performance across diverse tasks, ranging from speech recognition to general audio understanding. However, their scalability is limited by the quadratic complexity of attention and the high token rates of audio signals. These challenges make it difficult to extend LALMs to long-form audio and to deploy them on resource-constrained platforms such as edge devices. In this paper, we explore techniques such as unsupervised segmentation, uniform average pooling, etc., to reduce the number of audio tokens generated by the LALM's audio encoder but before they are consumed by the LLM decoder. To mitigate potential performance degradation introduced by the compressed representations, we employ low-rank adapters to finetune the model. We evaluate our proposed models on two tasks, automatic speech recognition and speech-to-speech translation tasks, that are dependent on effectively uncovering the underlying lexical content of the input signal and study the effect of downsampling on these tasks. Experimental results show that compressed LALMs can achieve performance closer to frame-level LALMs while reducing the input audio token count upto three times before the LLM backbone.
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            <a href="https://www.alphaxiv.org/abs/2511.20965v1" target="_blank" rel="noopener noreferrer">
                TrafficLens：基于大语言模型的多摄像头交通视频分析
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            TrafficLens: Multi-Camera Traffic Video Analysis Using LLMs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Md Adnan Arefeen, Biplob Debnath, Srimat Chakradhar
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的交通视频分析，虽然使用了LLMs技术，但应用场景是交通监控而非推荐系统、搜索或广告领域。多摄像头视频分析与RecSys/Search/Ads的核心业务需求关联度极低，无法直接应用于用户行为建模、内容排序或广告投放等关键任务。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 01:34:08
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20965v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20965v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.CL</span></div>
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                    Traffic cameras are essential in urban areas, playing a crucial role in intelligent transportation systems. Multiple cameras at intersections enhance law enforcement capabilities, traffic management, and pedestrian safety. However, efficiently managing and analyzing multi-camera feeds poses challenges due to the vast amount of data. Analyzing such huge video data requires advanced analytical tools. While Large Language Models (LLMs) like ChatGPT, equipped with retrieval-augmented generation (RAG) systems, excel in text-based tasks, integrating them into traffic video analysis demands converting video data into text using a Vision-Language Model (VLM), which is time-consuming and delays the timely utilization of traffic videos for generating insights and investigating incidents. To address these challenges, we propose TrafficLens, a tailored algorithm for multi-camera traffic intersections. TrafficLens employs a sequential approach, utilizing overlapping coverage areas of cameras. It iteratively applies VLMs with varying token limits, using previous outputs as prompts for subsequent cameras, enabling rapid generation of detailed textual descriptions while reducing processing time. Additionally, TrafficLens intelligently bypasses redundant VLM invocations through an object-level similarity detector. Experimental results with real-world datasets demonstrate that TrafficLens reduces video-to-text conversion time by up to $4\times$ while maintaining information accuracy.
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            <a href="https://www.alphaxiv.org/abs/2511.20940v1" target="_blank" rel="noopener noreferrer">
                Chatty-KG：一种用于知识图谱上按需对话式问答的多智能体AI系统
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            Chatty-KG: A Multi-Agent AI System for On-Demand Conversational Question Answering over Knowledge Graphs
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Reham Omar, Abdelghny Orogat, Ibrahim Abdelaziz, Omij Mangukiya, Panos Kalnis, E...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注知识图谱上的对话式问答系统，这属于通用对话AI领域，与推荐系统、搜索或广告的核心技术关联较弱。虽然多智能体系统可能有一些架构启发，但论文焦点是问答而非排名、检索或个性化推荐等核心RecSys/Search/Ads任务，潜在应用不明确。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 00:18:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20940v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20940v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Conversational Question Answering over Knowledge Graphs (KGs) combines the factual grounding of KG-based QA with the interactive nature of dialogue systems. KGs are widely used in enterprise and domain applications to provide structured, evolving, and reliable knowledge. Large language models (LLMs) enable natural and context-aware conversations, but lack direct access to private and dynamic KGs. Retrieval-augmented generation (RAG) systems can retrieve graph content but often serialize structure, struggle with multi-turn context, and require heavy indexing. Traditional KGQA systems preserve structure but typically support only single-turn QA, incur high latency, and struggle with coreference and context tracking. To address these limitations, we propose Chatty-KG, a modular multi-agent system for conversational QA over KGs. Chatty-KG combines RAG-style retrieval with structured execution by generating SPARQL queries through task-specialized LLM agents. These agents collaborate for contextual interpretation, dialogue tracking, entity and relation linking, and efficient query planning, enabling accurate and low-latency translation of natural questions into executable queries. Experiments on large and diverse KGs show that Chatty-KG significantly outperforms state-of-the-art baselines in both single-turn and multi-turn settings, achieving higher F1 and P@1 scores. Its modular design preserves dialogue coherence and supports evolving KGs without fine-tuning or pre-processing. Evaluations with commercial (e.g., GPT-4o, Gemini-2.0) and open-weight (e.g., Phi-4, Gemma 3) LLMs confirm broad compatibility and stable performance. Overall, Chatty-KG unifies conversational flexibility with structured KG grounding, offering a scalable and extensible approach for reliable multi-turn KGQA.
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            <a href="https://www.alphaxiv.org/abs/2511.20937v1" target="_blank" rel="noopener noreferrer">
                ENACT：通过以自我为中心交互的世界建模评估具身认知
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            ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qineng Wang, Wenlong Huang, Yu Zhou, Hang Yin, Tianwei Bao, Jianwen Lyu, Weiyu L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注具身认知和世界建模，属于机器人学和认知科学领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然世界建模概念在理论上可能启发某些序列建模方法，但这种连接过于间接且没有明确的实际应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 00:06:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20937v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20937v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.RO</span></div>
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                    Embodied cognition argues that intelligence arises from sensorimotor interaction rather than passive observation. It raises an intriguing question: do modern vision-language models (VLMs), trained largely in a disembodied manner, exhibit signs of embodied cognition? We introduce ENACT, a benchmark that casts evaluation of embodied cognition as world modeling from egocentric interaction in a visual question answering (VQA) format. Framed as a partially observable Markov decision process (POMDP) whose actions are scene graph changes, ENACT comprises two complementary sequence reordering tasks: forward world modeling (reorder shuffled observations given actions) and inverse world modeling (reorder shuffled actions given observations). While conceptually simple, solving these tasks implicitly demands capabilities central to embodied cognition-affordance recognition, action-effect reasoning, embodied awareness, and interactive, long-horizon memory from partially observable egocentric input, while avoiding low-level image synthesis that could confound the evaluation. We provide a scalable pipeline that synthesizes QA pairs from robotics simulation (BEHAVIOR) and evaluates models on 8,972 QA pairs spanning long-horizon home-scale activities. Experiments reveal a performance gap between frontier VLMs and humans that widens with interaction horizon. Models consistently perform better on the inverse task than the forward one and exhibit anthropocentric biases, including a preference for right-handed actions and degradation when camera intrinsics or viewpoints deviate from human vision. Website at https://enact-embodied-cognition.github.io/.
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            <a href="https://www.alphaxiv.org/abs/2511.21666v1" target="_blank" rel="noopener noreferrer">
                视觉物体姿态估计的不确定性量化
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Uncertainty Quantification for Visual Object Pose Estimation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lorenzo Shaikewitz, Charis Georgiou, Luca Carlone
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的姿态估计不确定性量化，属于纯粹的视觉技术范畴。虽然姿态估计在增强现实等应用中有价值，但论文标题没有显示出与推荐系统、搜索或广告中用户行为建模、内容排序或异构数据融合的直接关联。对于视觉模态的推荐应用来说，这种底层姿态不确定性量化的技术距离实际应用场景较远。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:39:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21666v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21666v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Quantifying the uncertainty of an object's pose estimate is essential for robust control and planning. Although pose estimation is a well-studied robotics problem, attaching statistically rigorous uncertainty is not well understood without strict distributional assumptions. We develop distribution-free pose uncertainty bounds about a given pose estimate in the monocular setting. Our pose uncertainty only requires high probability noise bounds on pixel detections of 2D semantic keypoints on a known object. This noise model induces an implicit, non-convex set of pose uncertainty constraints. Our key contribution is SLUE (S-Lemma Uncertainty Estimation), a convex program to reduce this set to a single ellipsoidal uncertainty bound that is guaranteed to contain the true object pose with high probability. SLUE solves a relaxation of the minimum volume bounding ellipsoid problem inspired by the celebrated S-lemma. It requires no initial guess of the bound's shape or size and is guaranteed to contain the true object pose with high probability. For tighter uncertainty bounds at the same confidence, we extend SLUE to a sum-of-squares relaxation hierarchy which is guaranteed to converge to the minimum volume ellipsoidal uncertainty bound for a given set of keypoint constraints. We show this pose uncertainty bound can easily be projected to independent translation and axis-angle orientation bounds. We evaluate SLUE on two pose estimation datasets and a real-world drone tracking scenario. Compared to prior work, SLUE generates substantially smaller translation bounds and competitive orientation bounds. We release code at https://github.com/MIT-SPARK/PoseUncertaintySets.
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            <a href="https://www.alphaxiv.org/abs/2511.21663v1" target="_blank" rel="noopener noreferrer">
                注意力引导的逐块稀疏对抗攻击在视觉-语言-动作模型上的应用
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Attention-Guided Patch-Wise Sparse Adversarial Attacks on Vision-Language-Action Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Naifu Zhang, Wei Tao, Xi Xiao, Qianpu Sun, Yuxin Zheng, Wentao Mo, Peiqiang Wang...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注对抗攻击和计算机视觉安全领域，与推荐系统、搜索或广告的核心技术进展无关。虽然提到了视觉-语言模型，但焦点是攻击方法而非建模技术，且没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:37:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21663v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21663v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    In recent years, Vision-Language-Action (VLA) models in embodied intelligence have developed rapidly. However, existing adversarial attack methods require costly end-to-end training and often generate noticeable perturbation patches. To address these limitations, we propose ADVLA, a framework that directly applies adversarial perturbations on features projected from the visual encoder into the textual feature space. ADVLA efficiently disrupts downstream action predictions under low-amplitude constraints, and attention guidance allows the perturbations to be both focused and sparse. We introduce three strategies that enhance sensitivity, enforce sparsity, and concentrate perturbations. Experiments demonstrate that under an $L_{\infty}=4/255$ constraint, ADVLA combined with Top-K masking modifies less than 10% of the patches while achieving an attack success rate of nearly 100%. The perturbations are concentrated on critical regions, remain almost imperceptible in the overall image, and a single-step iteration takes only about 0.06 seconds, significantly outperforming conventional patch-based attacks. In summary, ADVLA effectively weakens downstream action predictions of VLA models under low-amplitude and locally sparse conditions, avoiding the high training costs and conspicuous perturbations of traditional patch attacks, and demonstrates unique effectiveness and practical value for attacking VLA feature spaces.
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            <a href="https://www.alphaxiv.org/abs/2511.21662v1" target="_blank" rel="noopener noreferrer">
                Multi-Crit：基于多元化标准遵循的多模态评估器基准测试
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tianyi Xiong, Yi Ge, Ming Li, Zuolong Zhang, Pranav Kulkarni, Kaishen Wang, Qi H...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态评估基准测试，属于评估基准范畴，这在Irrelevant Topics中明确排除。虽然提到了多模态和标准遵循，但缺乏与推荐系统、搜索或广告的直接关联，也没有展示在Transformer架构效率或LLM技术应用方面的明确潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:35:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21662v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21662v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Large multimodal models (LMMs) are increasingly adopted as judges in multimodal evaluation systems due to their strong instruction following and consistency with human preferences. However, their ability to follow diverse, fine-grained evaluation criteria remains underexplored. We develop Multi-Crit, a benchmark for evaluating multimodal judges on their capacity to follow pluralistic criteria and produce reliable criterion-level judgments. Covering both open-ended generation and verifiable reasoning tasks, Multi-Crit is built through a rigorous data curation pipeline that gathers challenging response pairs with multi-criterion human annotations. It further introduces three novel metrics for systematically assessing pluralistic adherence, criterion-switching flexibility, and the ability to recognize criterion-level preference conflicts. Comprehensive analysis of 25 LMMs reveals that 1) proprietary models still struggle to maintain consistent adherence to pluralistic criteria--especially in open-ended evaluation; 2) open-source models lag further behind in flexibly following diverse criteria; and 3) critic fine-tuning with holistic judgment signals enhances visual grounding but fails to generalize to pluralistic criterion-level judgment. Additional analyses on reasoning fine-tuning, test-time scaling, and boundary consistency between open-source and proprietary models further probe the limits of current multimodal judges. As a pioneering study, Multi-Crit lays the foundation for building reliable and steerable multimodal AI evaluation.
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            <a href="https://www.alphaxiv.org/abs/2511.21652v1" target="_blank" rel="noopener noreferrer">
                低资源设备上的持续错误纠正
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        <div class="mb-2 text-base text-gray-700">
            Continual Error Correction on Low-Resource Devices
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kirill Paramonov, Mete Ozay, Aristeidis Mystakidis, Nikolaos Tsalikidis, Dimitri...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注低资源设备上的持续学习错误纠正，虽然涉及设备效率问题，但未明确与推荐系统、搜索或广告的核心技术相关。论文标题缺乏对Transformer架构、LLM应用或异构数据建模的具体提及，因此与当前关注点的直接关联性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:24:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21652v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21652v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    The proliferation of AI models in everyday devices has highlighted a critical challenge: prediction errors that degrade user experience. While existing solutions focus on error detection, they rarely provide efficient correction mechanisms, especially for resource-constrained devices. We present a novel system enabling users to correct AI misclassifications through few-shot learning, requiring minimal computational resources and storage. Our approach combines server-side foundation model training with on-device prototype-based classification, enabling efficient error correction through prototype updates rather than model retraining. The system consists of two key components: (1) a server-side pipeline that leverages knowledge distillation to transfer robust feature representations from foundation models to device-compatible architectures, and (2) a device-side mechanism that enables ultra-efficient error correction through prototype adaptation. We demonstrate our system's effectiveness on both image classification and object detection tasks, achieving over 50% error correction in one-shot scenarios on Food-101 and Flowers-102 datasets while maintaining minimal forgetting (less than 0.02%) and negligible computational overhead. Our implementation, validated through an Android demonstration app, proves the system's practicality in real-world scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.21625v1" target="_blank" rel="noopener noreferrer">
                基于图卷积网络的主动学习用于动作识别
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Active Learning for GCN-based Action Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hichem Sahbi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉领域的动作识别任务，虽然涉及图卷积网络（GCN）这一图神经网络技术，但缺乏与推荐系统、搜索或广告领域的直接关联。论文专注于主动学习在视觉任务中的应用，没有展示在异构数据建模或推荐系统场景中的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:51:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21625v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21625v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Despite the notable success of graph convolutional networks (GCNs) in skeleton-based action recognition, their performance often depends on large volumes of labeled data, which are frequently scarce in practical settings. To address this limitation, we propose a novel label-efficient GCN model. Our work makes two primary contributions. First, we develop a novel acquisition function that employs an adversarial strategy to identify a compact set of informative exemplars for labeling. This selection process balances representativeness, diversity, and uncertainty. Second, we introduce bidirectional and stable GCN architectures. These enhanced networks facilitate a more effective mapping between the ambient and latent data spaces, enabling a better understanding of the learned exemplar distribution. Extensive evaluations on two challenging skeleton-based action recognition benchmarks reveal significant improvements achieved by our label-efficient GCNs compared to prior work.
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            <a href="https://www.alphaxiv.org/abs/2511.21574v1" target="_blank" rel="noopener noreferrer">
                面向3D点云模型的多模态鲁棒提示蒸馏
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            Multimodal Robust Prompt Distillation for 3D Point Cloud Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xiang Gu, Liming Lu, Xu Zheng, Anan Du, Yongbin Zhou, Shuchao Pang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注3D点云模型的提示蒸馏技术，属于计算机视觉领域。虽然涉及多模态和提示学习概念，但3D点云处理与推荐系统、搜索或广告的核心技术栈关联度极低。论文的技术方法难以直接应用于文本或序列数据的推荐/搜索场景，且缺乏明确的跨领域应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:49:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21574v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21574v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Adversarial attacks pose a significant threat to learning-based 3D point cloud models, critically undermining their reliability in security-sensitive applications. Existing defense methods often suffer from (1) high computational overhead and (2) poor generalization ability across diverse attack types. To bridge these gaps, we propose a novel yet efficient teacher-student framework, namely Multimodal Robust Prompt Distillation (MRPD) for distilling robust 3D point cloud model. It learns lightweight prompts by aligning student point cloud model's features with robust embeddings from three distinct teachers: a vision model processing depth projections, a high-performance 3D model, and a text encoder. To ensure a reliable knowledge transfer, this distillation is guided by a confidence-gated mechanism which dynamically balances the contribution of all input modalities. Notably, since the distillation is all during the training stage, there is no additional computational cost at inference. Extensive experiments demonstrate that MRPD substantially outperforms state-of-the-art defense methods against a wide range of white-box and black-box attacks, while even achieving better performance on clean data. Our work presents a new, practical paradigm for building robust 3D vision systems by efficiently harnessing multimodal knowledge.
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            <a href="https://www.alphaxiv.org/abs/2511.21490v1" target="_blank" rel="noopener noreferrer">
                合并与边界：面向类别增量学习的权重直接操作
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            Merge and Bound: Direct Manipulations on Weights for Class Incremental Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taehoon Kim, Donghwan Jang, Bohyung Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注类别增量学习中的权重操作技术，属于机器学习中的持续学习领域。虽然增量学习在概念上可能与推荐系统中的用户兴趣演化相关，但该论文的标题明确聚焦于分类任务和权重操作，没有明确指向推荐、搜索或广告领域的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:24:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21490v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21490v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.LG</span></div>
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                    We present a novel training approach, named Merge-and-Bound (M&B) for Class Incremental Learning (CIL), which directly manipulates model weights in the parameter space for optimization. Our algorithm involves two types of weight merging: inter-task weight merging and intra-task weight merging. Inter-task weight merging unifies previous models by averaging the weights of models from all previous stages. On the other hand, intra-task weight merging facilitates the learning of current task by combining the model parameters within current stage. For reliable weight merging, we also propose a bounded update technique that aims to optimize the target model with minimal cumulative updates and preserve knowledge from previous tasks; this strategy reveals that it is possible to effectively obtain new models near old ones, reducing catastrophic forgetting. M&B is seamlessly integrated into existing CIL methods without modifying architecture components or revising learning objectives. We extensively evaluate our algorithm on standard CIL benchmarks and demonstrate superior performance compared to state-of-the-art methods.
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            <a href="https://www.alphaxiv.org/abs/2511.21439v1" target="_blank" rel="noopener noreferrer">
                EvRainDrop：基于超图引导的补全方法，用于实现帧与事件流的有效聚合
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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            EvRainDrop: HyperGraph-guided Completion for Effective Frame and Event Stream Aggregation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Futian Wang, Fan Zhang, Xiao Wang, Mengqi Wang, Dexing Huang, Jin Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的帧与事件流聚合问题，属于视觉模态处理范畴。虽然提到了超图引导的补全方法，但其核心应用场景是视觉数据处理，与推荐系统、搜索或广告领域的异构数据建模没有明确的直接关联。该技术可能在某些边缘视觉应用中有所价值，但缺乏明确的RecSys/Search/Ads应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:30:04
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21439v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21439v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Event cameras produce asynchronous event streams that are spatially sparse yet temporally dense. Mainstream event representation learning algorithms typically use event frames, voxels, or tensors as input. Although these approaches have achieved notable progress, they struggle to address the undersampling problem caused by spatial sparsity. In this paper, we propose a novel hypergraph-guided spatio-temporal event stream completion mechanism, which connects event tokens across different times and spatial locations via hypergraphs and leverages contextual information message passing to complete these sparse events. The proposed method can flexibly incorporate RGB tokens as nodes in the hypergraph within this completion framework, enabling multi-modal hypergraph-based information completion. Subsequently, we aggregate hypergraph node information across different time steps through self-attention, enabling effective learning and fusion of multi-modal features. Extensive experiments on both single- and multi-label event classification tasks fully validated the effectiveness of our proposed framework. The source code of this paper will be released on https://github.com/Event-AHU/EvRainDrop.
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            <a href="https://www.alphaxiv.org/abs/2511.21422v1" target="_blank" rel="noopener noreferrer">
                E-M3RF：一种等变多模态3D重组装框架
            </a>
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            E-M3RF: An Equivariant Multimodal 3D Re-assembly Framework
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adeela Islam, Stefano Fiorini, Manuel Lecha, Theodore Tsesmelis, Stuart James, P...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及多模态和3D数据处理，但其核心聚焦于3D重组装任务，这与推荐系统、搜索或广告的核心技术领域缺乏直接关联。3D重组装框架在RecSys/Search/Ads中的潜在应用非常有限，主要可能涉及3D产品展示或场景重建等边缘场景，而非核心排序或推荐算法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:12:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21422v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21422v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    3D reassembly is a fundamental geometric problem, and in recent years it has increasingly been challenged by deep learning methods rather than classical optimization. While learning approaches have shown promising results, most still rely primarily on geometric features to assemble a whole from its parts. As a result, methods struggle when geometry alone is insufficient or ambiguous, for example, for small, eroded, or symmetric fragments. Additionally, solutions do not impose physical constraints that explicitly prevent overlapping assemblies. To address these limitations, we introduce E-M3RF, an equivariant multimodal 3D reassembly framework that takes as input the point clouds, containing both point positions and colors of fractured fragments, and predicts the transformations required to reassemble them using SE(3) flow matching. Each fragment is represented by both geometric and color features: i) 3D point positions are encoded as rotationconsistent geometric features using a rotation-equivariant encoder, ii) the colors at each 3D point are encoded with a transformer. The two feature sets are then combined to form a multimodal representation. We experimented on four datasets: two synthetic datasets, Breaking Bad and Fantastic Breaks, and two real-world cultural heritage datasets, RePAIR and Presious, demonstrating that E-M3RF on the RePAIR dataset reduces rotation error by 23.1% and translation error by 13.2%, while Chamfer Distance decreases by 18.4% compared to competing methods.
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            <a href="https://www.alphaxiv.org/abs/2511.21375v1" target="_blank" rel="noopener noreferrer">
                基于边界框思考：通过强化学习微调增强时空视频定位
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            Thinking With Bounding Boxes: Enhancing Spatio-Temporal Video Grounding via Reinforcement Fine-Tuning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Gu, Haoji Zhang, Qihang Fan, Jingxuan Niu, Zhipeng Zhang, Libo Zhang, Guang ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的时空视频定位任务，涉及边界框预测和强化学习微调。虽然强化学习是技术组件，但论文专注于纯粹的视觉理解任务（视频中的时空定位），与推荐系统、搜索或广告中的排名、检索或用户建模没有明确的直接关联。该技术缺乏在RecSys/Search/Ads领域的明显应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:21:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21375v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21375v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Spatio-temporal video grounding (STVG) requires localizing a target object in untrimmed videos both temporally and spatially from natural language descriptions. Despite their strong language understanding, multimodal large language models (MLLMs) underperform on STVG due to misaligned training objectives and weak fine-grained region-word alignment in standard visual encoders. To address this, we propose STVG-o1, the first framework that enables off-the-shelf MLLMs to achieve state-of-the-art STVG performance without any architectural modifications. Our method introduces a bounding-box chain-of-thought mechanism that explicitly reasons about spatio-temporal locations in an intermediate step before producing the final prediction. We further design a multi-dimensional reinforcement reward function consisting of format, consistency, temporal, spatial, and think rewards, which provides geometry-aware supervision through reinforcement fine-tuning. Evaluated on HCSTVG-v1/v2 and VidSTG, STVG-o1 sets new state-of-the-art results on HCSTVG, outperforming the best task-specific method by 7.3\% m\_tIoU on HCSTVG-v1, matching specialized models on VidSTG, and surpassing all existing MLLM-based approaches by large margins. It also demonstrates strong open-vocabulary generalization across datasets, establishing MLLMs as viable and powerful backbones for precise spatio-temporal grounding. Our code and models will be released.
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            <a href="https://www.alphaxiv.org/abs/2511.21309v1" target="_blank" rel="noopener noreferrer">
                CaliTex：用于视图一致3D纹理生成的几何校准注意力机制
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
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        <div class="mb-2 text-base text-gray-700">
            CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenyu Liu, Hongze Chen, Jingzhi Bao, Lingting Zhu, Runze Zhang, Weikai Chen, Ze...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D纹理生成和计算机视觉领域，主要解决3D内容的视图一致性问题。虽然提到了注意力机制，但其应用场景（3D纹理生成）与推荐系统、搜索或广告的核心技术栈关联性较弱，缺乏明确的跨领域应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 11:53:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21309v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21309v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Despite major advances brought by diffusion-based models, current 3D texture generation systems remain hindered by cross-view inconsistency -- textures that appear convincing from one viewpoint often fail to align across others. We find that this issue arises from attention ambiguity, where unstructured full attention is applied indiscriminately across tokens and modalities, causing geometric confusion and unstable appearance-structure coupling. To address this, we introduce CaliTex, a framework of geometry-calibrated attention that explicitly aligns attention with 3D structure. It introduces two modules: Part-Aligned Attention that enforces spatial alignment across semantically matched parts, and Condition-Routed Attention which routes appearance information through geometry-conditioned pathways to maintain spatial fidelity. Coupled with a two-stage diffusion transformer, CaliTex makes geometric coherence an inherent behavior of the network rather than a byproduct of optimization. Empirically, CaliTex produces seamless and view-consistent textures and outperforms both open-source and commercial baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.21298v1" target="_blank" rel="noopener noreferrer">
                PathMamba：一种用于卫星图像中拓扑一致性道路分割的混合Mamba-Transformer架构
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            PathMamba: A Hybrid Mamba-Transformer for Topologically Coherent Road Segmentation in Satellite Imagery
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jules Decaestecker, Nicolas Vigne
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文虽然涉及Transformer架构的改进（混合Mamba-Transformer），但其应用场景是卫星图像中的道路分割，属于纯粹的计算机视觉任务。没有明确的连接表明该技术能够应用于推荐系统、搜索或广告领域，缺乏跨模态建模或序列处理方面的通用性潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 11:42:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21298v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21298v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Achieving both high accuracy and topological continuity in road segmentation from satellite imagery is a critical goal for applications ranging from urban planning to disaster response. State-of-the-art methods often rely on Vision Transformers, which excel at capturing global context, yet their quadratic complexity is a significant barrier to efficient deployment, particularly for on-board processing in resource-constrained platforms. In contrast, emerging State Space Models like Mamba offer linear-time efficiency and are inherently suited to modeling long, continuous structures. We posit that these architectures have complementary strengths. To this end, we introduce PathMamba, a novel hybrid architecture that integrates Mamba's sequential modeling with the Transformer's global reasoning. Our design strategically uses Mamba blocks to trace the continuous nature of road networks, preserving topological structure, while integrating Transformer blocks to refine features with global context. This approach yields topologically superior segmentation maps without the prohibitive scaling costs of pure attention-based models. Our experiments on the DeepGlobe Road Extraction and Massachusetts Roads datasets demonstrate that PathMamba sets a new state-of-the-art. Notably, it significantly improves topological continuity, as measured by the APLS metric, setting a new benchmark while remaining computationally competitive.
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                用于遥感多任务学习的视觉语言模型协同训练
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            Co-Training Vision Language Models for Remote Sensing Multi-task Learning
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qingyun Li, Shuran Ma, Junwei Luo, Yi Yu, Yue Zhou, Fengxiang Wang, Xudong Lu, X...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">虽然论文涉及视觉语言模型和多任务学习，但其应用领域是遥感（remote sensing），这属于地理信息/地球观测的特定领域应用，与推荐系统、搜索或广告没有直接关联。论文没有显示出将VLM技术应用于异构数据建模或推荐/搜索场景的潜力。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:55:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21272v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21272v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    With Transformers achieving outstanding performance on individual remote sensing (RS) tasks, we are now approaching the realization of a unified model that excels across multiple tasks through multi-task learning (MTL). Compared to single-task approaches, MTL methods offer improved generalization, enhanced scalability, and greater practical applicability. Recently, vision language models (VLMs) have achieved promising results in RS image understanding, grounding, and ultra-high-resolution (UHR) image reasoning, respectively. Moreover, the unified text-based interface demonstrates significant potential for MTL. Hence, in this work, we present RSCoVLM, a simple yet flexible VLM baseline for RS MTL. Firstly, we create the data curation engine, including data acquisition, offline processing and integrating, as well as online loading and weighting. This data engine effectively addresses complex RS data enviroment and generates flexible vision-language conversations. Furthermore, we propose a unified dynamic-resolution strategy to address the diverse image scales inherent in RS imagery. For UHR images, we introduce the Zoom-in Chain mechanism together with its corresponding dataset, LRS-VQA-Zoom. The strategies are flexible and effectively mitigate the computational burdens. Additionally, we significantly enhance the model's object detection capability and propose a novel evaluation protocol that ensures fair comparison between VLMs and conventional detection models. Extensive experiments demonstrate that RSCoVLM achieves state-of-the-art performance across diverse tasks, outperforming existing RS VLMs and even rivaling specialized expert models. All the training and evaluating tools, model weights, and datasets have been fully open-sourced to support reproducibility. We expect that this baseline will promote further progress toward general-purpose RS models.
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            <a href="https://www.alphaxiv.org/abs/2511.21270v1" target="_blank" rel="noopener noreferrer">
                用于稳定且韵律丰富的单码本TTS大语言模型的大规模多奖励GRPO方法
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            Multi-Reward GRPO for Stable and Prosodic Single-Codebook TTS LLMs at Scale
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yicheng Zhong, Peiji Yang, Zhisheng Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本转语音(TTS)技术，属于语音生成领域，与搜索、推荐或广告系统的核心需求没有直接关联。虽然提到了LLM和大规模训练，但其应用场景局限于语音合成，缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:50:17
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                <a href="https://arxiv.org/abs/2511.21270v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21270v1
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                    Recent advances in Large Language Models (LLMs) have transformed text-to-speech (TTS) synthesis, inspiring autoregressive frameworks that represent speech as sequences of discrete codec tokens. Among them, single-codebook TTS LLMs have emerged as compact and streamable architectures that jointly model semantic and acoustic integration. However, despite their efficiency, these models often exhibit unstable prosody, speaker drift, and degraded naturalness. To address these issues, we propose a multi-reward Group Relative Policy Optimization (GRPO) framework that directly optimizes the token generation policy of single-codebook TTS LLMs. Beyond standard intelligibility and speaker similarity objectives, our design integrates three rule-based rewards: a length penalty for duration consistency, an entropy regularization reward for decoding stability, and an LLM-annotated prosody alignment reward that explicitly supervises rhythm. In this prosody reward, an external reasoning LLM predicts multiple plausible pause structures via in-context learning, providing a human-preference-aligned supervisory signal for GRPO training. To assess universality, we further attach a flow-matching (FM) decoder on top of the GRPO-optimized AR backbone and observe consistent additional gains, indicating that our reinforcement optimization enhances the intrinsic AR policy. We further conduct a scalability analysis across data sizes and model scales, revealing that the proposed method consistently enhances prosodic stability, speaker similarity, and overall speech naturalness in single-codebook TTS LLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.21265v1" target="_blank" rel="noopener noreferrer">
                通过高斯泼溅解锁半密集图像匹配的零样本潜力
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            Unlocking Zero-shot Potential of Semi-dense Image Matching via Gaussian Splatting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Juncheng Chen, Chao Xu, Yanjun Cao
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注计算机视觉中的图像匹配技术，属于纯粹的视觉领域研究。虽然高斯泼溅和零样本学习是前沿技术，但论文标题没有显示出与推荐系统、搜索或广告的直接关联，也没有表明这些技术如何应用于处理异构数据或多模态建模。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:43:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21265v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21265v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Learning-based image matching critically depends on large-scale, diverse, and geometrically accurate training data. 3D Gaussian Splatting (3DGS) enables photorealistic novel-view synthesis and thus is attractive for data generation. However, its geometric inaccuracies and biased depth rendering currently prevent robust correspondence labeling. To address this, we introduce MatchGS, the first framework designed to systematically correct and leverage 3DGS for robust, zero-shot image matching. Our approach is twofold: (1) a geometrically-faithful data generation pipeline that refines 3DGS geometry to produce highly precise correspondence labels, enabling the synthesis of a vast and diverse range of viewpoints without compromising rendering fidelity; and (2) a 2D-3D representation alignment strategy that infuses 3DGS' explicit 3D knowledge into the 2D matcher, guiding 2D semi-dense matchers to learn viewpoint-invariant 3D representations. Our generated ground-truth correspondences reduce the epipolar error by up to 40 times compared to existing datasets, enable supervision under extreme viewpoint changes, and provide self-supervisory signals through Gaussian attributes. Consequently, state-of-the-art matchers trained solely on our data achieve significant zero-shot performance gains on public benchmarks, with improvements of up to 17.7%. Our work demonstrates that with proper geometric refinement, 3DGS can serve as a scalable, high-fidelity, and structurally-rich data source, paving the way for a new generation of robust zero-shot image matchers.
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                平移等变复数值卷积神经网络
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            Shift-Equivariant Complex-Valued Convolutional Neural Networks
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Quentin Gabot, Teck-Yian Lim, Jérémy Fix, Joana Frontera-Pons, Chengfang Ren, Je...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究卷积神经网络的数学特性和复数表示，属于底层神经网络架构的改进。虽然卷积网络在推荐系统中用于序列建模有一定应用，但该论文的平移等变性和复数表示特性与推荐、搜索或广告系统的核心需求关联较弱，缺乏明确的直接应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:29:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21250v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21250v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Convolutional neural networks have shown remarkable performance in recent years on various computer vision problems. However, the traditional convolutional neural network architecture lacks a critical property: shift equivariance and invariance, broken by downsampling and upsampling operations. Although data augmentation techniques can help the model learn the latter property empirically, a consistent and systematic way to achieve this goal is by designing downsampling and upsampling layers that theoretically guarantee these properties by construction. Adaptive Polyphase Sampling (APS) introduced the cornerstone for shift invariance, later extended to shift equivariance with Learnable Polyphase up/downsampling (LPS) applied to real-valued neural networks. In this paper, we extend the work on LPS to complex-valued neural networks both from a theoretical perspective and with a novel building block of a projection layer from $\mathbb{C}$ to $\mathbb{R}$ before the Gumbel Softmax. We finally evaluate this extension on several computer vision problems, specifically for either the invariance property in classification tasks or the equivariance property in both reconstruction and semantic segmentation problems, using polarimetric Synthetic Aperture Radar images.
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            <a href="https://www.alphaxiv.org/abs/2511.21215v1" target="_blank" rel="noopener noreferrer">
                从扩散到一步生成：基于流的模型比较研究及其在图像修复中的应用
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            From Diffusion to One-Step Generation: A Comparative Study of Flow-Based Models with Application to Image Inpainting
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Umang Agarwal, Rudraksh Sangore, Sumit Laddha
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要研究扩散模型和流模型的比较，应用领域为图像修复，这属于纯粹的计算机视觉任务。虽然生成模型技术有潜在价值，但论文没有明确展示与推荐系统、搜索或广告的关联。图像修复应用与排名、个性化或用户行为建模等核心领域没有直接联系。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:44:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21215v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21215v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    We present a comprehensive comparative study of three generative modeling paradigms: Denoising Diffusion Probabilistic Models (DDPM), Conditional Flow Matching (CFM), and MeanFlow. While DDPM and CFM require iterative sampling, MeanFlow enables direct one-step generation by modeling the average velocity over time intervals. We implement all three methods using a unified TinyUNet architecture (<1.5M parameters) on CIFAR-10, demonstrating that CFM achieves an FID of 24.15 with 50 steps, significantly outperforming DDPM (FID 402.98). MeanFlow achieves FID 29.15 with single-step sampling -- a 50X reduction in inference time. We further extend CFM to image inpainting, implementing mask-guided sampling with four mask types (center, random bbox, irregular, half). Our fine-tuned inpainting model achieves substantial improvements: PSNR increases from 4.95 to 8.57 dB on center masks (+73%), and SSIM improves from 0.289 to 0.418 (+45%), demonstrating the effectiveness of inpainting-aware training.
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            <a href="https://www.alphaxiv.org/abs/2511.21202v1" target="_blank" rel="noopener noreferrer">
                面向细粒度视频动作识别的有效动作区域跟踪框架研究
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            Towards an Effective Action-Region Tracking Framework for Fine-grained Video Action Recognition
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Baoli Sun, Yihan Wang, Xinzhu Ma, Zhihui Wang, Kun Lu, Zhiyong Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的细粒度视频动作识别，虽然动作识别在理论上可能与某些推荐或搜索场景相关（如视频内容理解），但论文标题明确聚焦于视觉动作区域跟踪这一具体技术，缺乏与推荐系统、搜索或广告的直接联系。没有明确的机制表明该技术能够应用于异构数据建模或Transformer架构改进等当前关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:32:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21202v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21202v1
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                    Fine-grained action recognition (FGAR) aims to identify subtle and distinctive differences among fine-grained action categories. However, current recognition methods often capture coarse-grained motion patterns but struggle to identify subtle details in local regions evolving over time. In this work, we introduce the Action-Region Tracking (ART) framework, a novel solution leveraging a query-response mechanism to discover and track the dynamics of distinctive local details, enabling effective distinction of similar actions. Specifically, we propose a region-specific semantic activation module that employs discriminative and text-constrained semantics as queries to capture the most action-related region responses in each video frame, facilitating interaction among spatial and temporal dimensions with corresponding video features. The captured region responses are organized into action tracklets, which characterize region-based action dynamics by linking related responses across video frames in a coherent sequence. The text-constrained queries encode nuanced semantic representations derived from textual descriptions of action labels extracted by language branches within Visual Language Models (VLMs). To optimize the action tracklets, we design a multi-level tracklet contrastive constraint among region responses at spatial and temporal levels, enabling effective discrimination within each frame and correlation between adjacent frames. Additionally, a task-specific fine-tuning mechanism refines textual semantics such that semantic representations encoded by VLMs are preserved while optimized for task preferences. Comprehensive experiments on widely used action recognition benchmarks demonstrate the superiority to previous state-of-the-art baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.21193v1" target="_blank" rel="noopener noreferrer">
                你可以信任你的聚类模型：一种用于深度聚类的参数自由自增强插件
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            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            You Can Trust Your Clustering Model: A Parameter-free Self-Boosting Plug-in for Deep Clustering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hanyang Li, Yuheng Jia, Hui Liu, Junhui Hou
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注深度聚类方法的改进，属于无监督学习领域。虽然聚类技术在推荐系统中可用于用户分群或物品分类，但该论文标题强调的参数自由自增强插件更偏向通用的聚类算法优化，没有明确指向推荐、搜索或广告领域的特定应用或技术突破。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:16:36
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21193v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21193v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Recent deep clustering models have produced impressive clustering performance. However, a common issue with existing methods is the disparity between global and local feature structures. While local structures typically show strong consistency and compactness within class samples, global features often present intertwined boundaries and poorly separated clusters. Motivated by this observation, we propose DCBoost, a parameter-free plug-in designed to enhance the global feature structures of current deep clustering models. By harnessing reliable local structural cues, our method aims to elevate clustering performance effectively. Specifically, we first identify high-confidence samples through adaptive $k$-nearest neighbors-based consistency filtering, aiming to select a sufficient number of samples with high label reliability to serve as trustworthy anchors for self-supervision. Subsequently, these samples are utilized to compute a discriminative loss, which promotes both intra-class compactness and inter-class separability, to guide network optimization. Extensive experiments across various benchmark datasets showcase that our DCBoost significantly improves the clustering performance of diverse existing deep clustering models. Notably, our method improves the performance of current state-of-the-art baselines (e.g., ProPos) by more than 3% and amplifies the silhouette coefficient by over $7\times$. Code is available at <https://github.com/l-h-y168/DCBoost>.
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            <a href="https://www.alphaxiv.org/abs/2511.21191v1" target="_blank" rel="noopener noreferrer">
                场景作为令牌：用于通用3D视觉语言理解的多尺度正态分布变换分词器
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            Scenes as Tokens: Multi-Scale Normal Distributions Transform Tokenizer for General 3D Vision-Language Understanding
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yutao Tang, Cheng Zhao, Gaurav Mittal, Rohith Kukkala, Rama Chellappa, Cheng Pen...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D视觉语言理解，属于纯粹的视觉领域研究，没有明确展示与推荐系统、搜索或广告的关联。虽然标题提到'视觉语言理解'，但明确的3D视觉焦点和缺乏对推荐/搜索/广告应用的提及使其相关性较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:12:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21191v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21191v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recent advances in 3D vision-language models (VLMs) highlight a strong potential for 3D scene understanding and reasoning. However, effectively tokenizing 3D scenes into holistic scene tokens, and leveraging these tokens across diverse 3D understanding tasks, remain highly challenging. We present NDTokenizer3D, a generalist 3D VLM that performs a wide range of 3D scene understanding tasks while naturally supporting human interactions, thereby bridging language-level reasoning with 3D spatial understanding. The core of our approach is a novel three-stage scene tokenization pipeline built upon a Multi-Scale Normal Distributions Transform (NDT) representation, paired with a Multi-Scale NDT Decoder (MSDec). Specifically, NDTokenizer3D first constructs a multi-scale NDT representation from raw high-resolution point clouds, preserving both global context and fine-grained geometric details. Next, the MSDec progressively fuses cross-scale NDT features, producing holistic scene tokens consumable by LLM endpoints. Beyond tokenization, MSDec is repurposed as a general interface for human-interactive prompting (points, boxes, masks) and segmentation-mask decoding, unifying diverse 3D scene understanding tasks within a single architecture. With this compact and unified design, NDTokenizer3D offers a fine-grained, general-purpose 3D VLM, achieving remarkable improvements in 3D Referring Segmentation, 3D Visual Question Answering, and 3D Dense Captioning.
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            <a href="https://www.alphaxiv.org/abs/2511.21185v1" target="_blank" rel="noopener noreferrer">
                渐进式进步：自回归图像生成的测试时缩放
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            Progress by Pieces: Test-Time Scaling for Autoregressive Image Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joonhyung Park, Hyeongwon Jang, Joowon Kim, Eunho Yang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于自回归图像生成方法，属于纯粹的视觉生成领域，与推荐系统、搜索或广告的核心排名任务没有直接关联。虽然测试时缩放技术可能具有一般性价值，但论文标题明确限定在图像生成应用场景，没有表明对异构数据处理或推荐/搜索/广告系统的潜在适用性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:01:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21185v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21185v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent visual autoregressive (AR) models have shown promising capabilities in text-to-image generation, operating in a manner similar to large language models. While test-time computation scaling has brought remarkable success in enabling reasoning-enhanced outputs for challenging natural language tasks, its adaptation to visual AR models remains unexplored and poses unique challenges. Naively applying test-time scaling strategies such as Best-of-N can be suboptimal: they consume full-length computation on erroneous generation trajectories, while the raster-scan decoding scheme lacks a blueprint of the entire canvas, limiting scaling benefits as only a few prompt-aligned candidates are generated. To address these, we introduce GridAR, a test-time scaling framework designed to elicit the best possible results from visual AR models. GridAR employs a grid-partitioned progressive generation scheme in which multiple partial candidates for the same position are generated within a canvas, infeasible ones are pruned early, and viable ones are fixed as anchors to guide subsequent decoding. Coupled with this, we present a layout-specified prompt reformulation strategy that inspects partial views to infer a feasible layout for satisfying the prompt. The reformulated prompt then guides subsequent image generation to mitigate the blueprint deficiency. Together, GridAR achieves higher-quality results under limited test-time scaling: with N=4, it even outperforms Best-of-N (N=8) by 14.4% on T2I-CompBench++ while reducing cost by 25.6%. It also generalizes to autoregressive image editing, showing comparable edit quality and a 13.9% gain in semantic preservation on PIE-Bench over larger-N baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.21150v1" target="_blank" rel="noopener noreferrer">
                LLaVA-UHD v3：面向多模态大语言模型的高效原生分辨率编码的渐进式视觉压缩
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            LLaVA-UHD v3: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shichu Sun, Yichen Zhang, Haolin Song, Zonghao Guo, Chi Chen, Yidan Zhang, Yuan ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注多模态大语言模型中的视觉压缩技术，属于纯粹的视觉模态优化。虽然提到了MLLMs，但核心贡献是视觉编码效率的提升，没有明确展示在推荐系统、搜索或广告中的潜在应用。视觉压缩技术本身与文本序列建模、用户行为预测等RecSys/Search/Ads核心问题关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 08:11:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21150v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21150v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Visual encoding followed by token condensing has become the standard architectural paradigm in multi-modal large language models (MLLMs). Many recent MLLMs increasingly favor global native- resolution visual encoding over slice-based methods. To investigate this trend, we systematically compare their behavior on vision-language understanding and attention patterns, revealing that global encoding enhances overall capability but at the expense of greater computational overhead. To address this issue, we present LLaVA-UHD v3, an MLLM centered upon our proposed Progressive Visual Compression (PVC) method, which can be seamlessly integrated into standard Vision Transformer (ViT) to enable efficient native-resolution encoding. The PVC approach consists of two key modules: (i) refined patch embedding, which supports flexible patch-size scaling for fine-grained visual model- ing, (ii) windowed token compression, hierarchically deployed across ViT layers to progressively aggregate local token representations. Jointly modulated by these two modules, a widely pretrained ViT can be reconfigured into an efficient architecture while largely preserving generality. Evaluated across extensive benchmarks, the transformed ViT, termed ViT-UHD, demonstrates competitive performance with MoonViT while reducing TTFT (time-to-first-token) by 2.4x, when developed within an identical MLLM architecture. Building upon ViT-UHD, LLaVA-UHD v3 also achieves competitive performance to Qwen2-VL, while further reducing TTFT by 1.9x. We will release all code and checkpoints to support future research on efficient MLLMs.
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            <a href="https://www.alphaxiv.org/abs/2511.21146v1" target="_blank" rel="noopener noreferrer">
                AV-Edit：通过音频-视觉语义联合控制的多模态生成式音效编辑
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            AV-Edit: Multimodal Generative Sound Effect Editing via Audio-Visual Semantic Joint Control
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xinyue Guo, Xiaoran Yang, Lipan Zhang, Jianxuan Yang, Zhao Wang, Jian Luan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注音频-视觉多模态生成和音效编辑，属于多媒体内容生成领域。虽然涉及多模态建模，但其应用场景（音效编辑）与推荐系统、搜索或广告的核心技术没有直接关联，也不涉及Transformer架构改进或LLM技术在推荐/搜索领域的潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:59:53
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21146v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21146v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.MM</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.SD</span></div>
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                    Sound effect editing-modifying audio by adding, removing, or replacing elements-remains constrained by existing approaches that rely solely on low-level signal processing or coarse text prompts, often resulting in limited flexibility and suboptimal audio quality. To address this, we propose AV-Edit, a generative sound effect editing framework that enables fine-grained editing of existing audio tracks in videos by jointly leveraging visual, audio, and text semantics. Specifically, the proposed method employs a specially designed contrastive audio-visual masking autoencoder (CAV-MAE-Edit) for multimodal pre-training, learning aligned cross-modal representations. These representations are then used to train an editorial Multimodal Diffusion Transformer (MM-DiT) capable of removing visually irrelevant sounds and generating missing audio elements consistent with video content through a correlation-based feature gating training strategy. Furthermore, we construct a dedicated video-based sound editing dataset as an evaluation benchmark. Experiments demonstrate that the proposed AV-Edit generates high-quality audio with precise modifications based on visual content, achieving state-of-the-art performance in the field of sound effect editing and exhibiting strong competitiveness in the domain of audio generation.
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            <a href="https://www.alphaxiv.org/abs/2511.21129v1" target="_blank" rel="noopener noreferrer">
                CtrlVDiff：通过统一多模态视频扩散实现可控视频生成
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            CtrlVDiff: Controllable Video Generation via Unified Multimodal Video Diffusion
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dianbing Xi, Jiepeng Wang, Yuanzhi Liang, Xi Qiu, Jialun Liu, Hao Pan, Yuchi Huo...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于可控视频生成技术，这属于纯粹的视觉内容生成领域。虽然标题中提到'多模态'和'可控生成'，但核心是视频生成而非推荐、搜索或广告系统。论文没有显示出与推荐系统、搜索排名或广告优化的直接关联，也不涉及Transformer架构改进或LLM技术在推荐领域的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:27:11
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21129v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21129v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.GR</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    We tackle the dual challenges of video understanding and controllable video generation within a unified diffusion framework. Our key insights are two-fold: geometry-only cues (e.g., depth, edges) are insufficient: they specify layout but under-constrain appearance, materials, and illumination, limiting physically meaningful edits such as relighting or material swaps and often causing temporal drift. Enriching the model with additional graphics-based modalities (intrinsics and semantics) provides complementary constraints that both disambiguate understanding and enable precise, predictable control during generation. However, building a single model that uses many heterogeneous cues introduces two core difficulties. Architecturally, the model must accept any subset of modalities, remain robust to missing inputs, and inject control signals without sacrificing temporal consistency. Data-wise, training demands large-scale, temporally aligned supervision that ties real videos to per-pixel multimodal annotations. We then propose CtrlVDiff, a unified diffusion model trained with a Hybrid Modality Control Strategy (HMCS) that routes and fuses features from depth, normals, segmentation, edges, and graphics-based intrinsics (albedo, roughness, metallic), and re-renders videos from any chosen subset with strong temporal coherence. To enable this, we build MMVideo, a hybrid real-and-synthetic dataset aligned across modalities and captions. Across understanding and generation benchmarks, CtrlVDiff delivers superior controllability and fidelity, enabling layer-wise edits (relighting, material adjustment, object insertion) and surpassing state-of-the-art baselines while remaining robust when some modalities are unavailable.
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            <a href="https://www.alphaxiv.org/abs/2511.21087v1" target="_blank" rel="noopener noreferrer">
                MIRA：用于图像编辑的多模态迭代推理智能体
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>2/10
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        <div class="mb-2 text-base text-gray-700">
            MIRA: Multimodal Iterative Reasoning Agent for Image Editing
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyun Zeng, Hang Hua, Jiebo Luo
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像编辑任务，属于纯粹的视觉应用领域。虽然涉及多模态和推理技术，但缺乏与推荐系统、搜索或广告的直接关联。图像编辑本身属于AIGC和内容生成范畴，这些已被明确列为不相关主题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:13:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21087v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21087v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Instruction-guided image editing offers an intuitive way for users to edit images with natural language. However, diffusion-based editing models often struggle to accurately interpret complex user instructions, especially those involving compositional relationships, contextual cues, or referring expressions, leading to edits that drift semantically or fail to reflect the intended changes. We tackle this problem by proposing MIRA (Multimodal Iterative Reasoning Agent), a lightweight, plug-and-play multimodal reasoning agent that performs editing through an iterative perception-reasoning-action loop, effectively simulating multi-turn human-model interaction processes. Instead of issuing a single prompt or static plan, MIRA predicts atomic edit instructions step by step, using visual feedback to make its decisions. Our 150K multimodal tool-use dataset, MIRA-Editing, combined with a two-stage SFT + GRPO training pipeline, enables MIRA to perform reasoning and editing over complex editing instructions. When paired with open-source image editing models such as Flux.1-Kontext, Step1X-Edit, and Qwen-Image-Edit, MIRA significantly improves both semantic consistency and perceptual quality, achieving performance comparable to or exceeding proprietary systems such as GPT-Image and Nano-Banana.
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            <a href="https://www.alphaxiv.org/abs/2511.21064v1" target="_blank" rel="noopener noreferrer">
                OVOD-Agent：一种用于主动视觉推理与自演进检测的马尔可夫-老虎机框架
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            OVOD-Agent: A Markov-Bandit Framework for Proactive Visual Reasoning and Self-Evolving Detection
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chujie Wang, Jianyu Lu, Zhiyuan Luo, Xi Chen, Chu He
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文主要关注视觉推理和检测任务，属于计算机视觉领域而非推荐系统、搜索或广告的核心技术。虽然马尔可夫决策过程和老虎机框架在推荐系统中用于探索-利用权衡，但论文的视觉焦点和具体应用场景与当前关注领域关联度较低。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 05:08:26
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21064v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21064v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Open-Vocabulary Object Detection (OVOD) aims to enable detectors to generalize across categories by leveraging semantic information. Although existing methods are pretrained on large vision-language datasets, their inference is still limited to fixed category names, creating a gap between multimodal training and unimodal inference. Previous work has shown that improving textual representation can significantly enhance OVOD performance, indicating that the textual space is still underexplored. To this end, we propose OVOD-Agent, which transforms passive category matching into proactive visual reasoning and self-evolving detection. Inspired by the Chain-of-Thought (CoT) paradigm, OVOD-Agent extends the textual optimization process into an interpretable Visual-CoT with explicit actions. OVOD's lightweight nature makes LLM-based management unsuitable; instead, we model visual context transitions as a Weakly Markovian Decision Process (w-MDP) over eight state spaces, which naturally represents the agent's state, memory, and interaction dynamics. A Bandit module generates exploration signals under limited supervision, helping the agent focus on uncertain regions and adapt its detection policy. We further integrate Markov transition matrices with Bandit trajectories for self-supervised Reward Model (RM) optimization, forming a closed loop from Bandit exploration to RM learning. Experiments on COCO and LVIS show that OVOD-Agent provides consistent improvements across OVOD backbones, particularly on rare categories, confirming the effectiveness of the proposed framework.
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            <a href="https://www.alphaxiv.org/abs/2511.21053v1" target="_blank" rel="noopener noreferrer">
                AerialMind：面向无人机场景的指代多目标追踪
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            AerialMind: Towards Referring Multi-Object Tracking in UAV Scenarios
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chenglizhao Chen, Shaofeng Liang, Runwei Guan, Xiaolou Sun, Haocheng Zhao, Haiyu...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于无人机场景下的多目标追踪技术，属于计算机视觉领域。虽然多目标追踪在技术上有一定通用性，但论文标题明确指向无人机特定应用场景，与推荐系统、搜索或广告的核心技术关联度极低。没有明确证据表明该技术能够直接应用于或启发推荐系统、搜索或广告领域的创新。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:44:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21053v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21053v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Referring Multi-Object Tracking (RMOT) aims to achieve precise object detection and tracking through natural language instructions, representing a fundamental capability for intelligent robotic systems. However, current RMOT research remains mostly confined to ground-level scenarios, which constrains their ability to capture broad-scale scene contexts and perform comprehensive tracking and path planning. In contrast, Unmanned Aerial Vehicles (UAVs) leverage their expansive aerial perspectives and superior maneuverability to enable wide-area surveillance. Moreover, UAVs have emerged as critical platforms for Embodied Intelligence, which has given rise to an unprecedented demand for intelligent aerial systems capable of natural language interaction. To this end, we introduce AerialMind, the first large-scale RMOT benchmark in UAV scenarios, which aims to bridge this research gap. To facilitate its construction, we develop an innovative semi-automated collaborative agent-based labeling assistant (COALA) framework that significantly reduces labor costs while maintaining annotation quality. Furthermore, we propose HawkEyeTrack (HETrack), a novel method that collaboratively enhances vision-language representation learning and improves the perception of UAV scenarios. Comprehensive experiments validated the challenging nature of our dataset and the effectiveness of our method.
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            <a href="https://www.alphaxiv.org/abs/2511.21028v1" target="_blank" rel="noopener noreferrer">
                基于标量条件化的深度参数插值方法
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            Deep Parameter Interpolation for Scalar Conditioning
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chicago Y. Park, Michael T. McCann, Cristina Garcia-Cardona, Brendt Wohlberg, Ul...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题暗示了参数插值或模型融合技术，可能涉及模型权重插值或条件生成。虽然参数插值在模型集成或条件生成中有应用，但该标题未明确表明与推荐系统、搜索或广告的直接关联，也未提及Transformer架构或LLM技术。其潜在应用场景过于宽泛，缺乏明确的RecSys/Search/Ads针对性。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:52:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21028v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21028v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">eess.IV</span><span class="category-tag">cs.CV</span></div>
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                    We propose deep parameter interpolation (DPI), a general-purpose method for transforming an existing deep neural network architecture into one that accepts an additional scalar input. Recent deep generative models, including diffusion models and flow matching, employ a single neural network to learn a time- or noise level-dependent vector field. Designing a network architecture to accurately represent this vector field is challenging because the network must integrate information from two different sources: a high-dimensional vector (usually an image) and a scalar. Common approaches either encode the scalar as an additional image input or combine scalar and vector information in specific network components, which restricts architecture choices. Instead, we propose to maintain two learnable parameter sets within a single network and to introduce the scalar dependency by dynamically interpolating between the parameter sets based on the scalar value during training and sampling. DPI is a simple, architecture-agnostic method for adding scalar dependence to a neural network. We demonstrate that our method improves denoising performance and enhances sample quality for both diffusion and flow matching models, while achieving computational efficiency comparable to standard scalar conditioning techniques. Code is available at https://github.com/wustl-cig/parameter_interpolation.
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            <a href="https://www.alphaxiv.org/abs/2511.21007v1" target="_blank" rel="noopener noreferrer">
                MetaRank：面向任务感知的度量选择用于模型可迁移性估计
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            MetaRank: Task-Aware Metric Selection for Model Transferability Estimation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuhang Liu, Wenjie Zhao, Yunhui Guo
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文关注模型可迁移性估计中的度量选择问题，属于通用的机器学习方法研究。虽然模型可迁移性在推荐系统和搜索中有潜在应用（如跨域推荐），但论文标题未明确指向推荐/搜索/广告领域的具体应用场景，也未涉及LLM、Transformer架构或异构数据建模等核心技术方向。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:15:13
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21007v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21007v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Selecting an appropriate pre-trained source model is a critical, yet computationally expensive, task in transfer learning. Model Transferability Estimation (MTE) methods address this by providing efficient proxy metrics to rank models without full fine-tuning. In practice, the choice of which MTE metric to use is often ad hoc or guided simply by a metric's average historical performance. However, we observe that the effectiveness of MTE metrics is highly task-dependent and no single metric is universally optimal across all target datasets. To address this gap, we introduce MetaRank, a meta-learning framework for automatic, task-aware MTE metric selection. We formulate metric selection as a learning-to-rank problem. Rather than relying on conventional meta-features, MetaRank encodes textual descriptions of both datasets and MTE metrics using a pretrained language model, embedding them into a shared semantic space. A meta-predictor is then trained offline on diverse meta-tasks to learn the intricate relationship between dataset characteristics and metric mechanisms, optimized with a listwise objective that prioritizes correctly ranking the top-performing metrics. During the subsequent online phase, MetaRank efficiently ranks the candidate MTE metrics for a new, unseen target dataset based on its textual description, enabling practitioners to select the most appropriate metric a priori. Extensive experiments across 11 pretrained models and 11 target datasets demonstrate the strong effectiveness of our approach.
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            <a href="https://www.alphaxiv.org/abs/2511.20996v1" target="_blank" rel="noopener noreferrer">
                从图像修复到图层分解：重新利用生成式图像修复模型进行图像图层分解
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            From Inpainting to Layer Decomposition: Repurposing Generative Inpainting Models for Image Layer Decomposition
        </div>
        
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jingxi Chen, Yixiao Zhang, Xiaoye Qian, Zongxia Li, Cornelia Fermuller, Caren Ch...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文主要关注计算机视觉中的图像修复和图层分解技术，属于纯粹的视觉处理范畴。虽然图层分解在概念上可能与推荐系统中的多模态数据处理有微弱联系，但论文的核心内容与RecSys/Search/Ads领域没有明确的直接关联，也没有展示出在推荐、搜索或广告中的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:50:07
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20996v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20996v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Images can be viewed as layered compositions, foreground objects over background, with potential occlusions. This layered representation enables independent editing of elements, offering greater flexibility for content creation. Despite the progress in large generative models, decomposing a single image into layers remains challenging due to limited methods and data. We observe a strong connection between layer decomposition and in/outpainting tasks, and propose adapting a diffusion-based inpainting model for layer decomposition using lightweight finetuning. To further preserve detail in the latent space, we introduce a novel multi-modal context fusion module with linear attention complexity. Our model is trained purely on a synthetic dataset constructed from open-source assets and achieves superior performance in object removal and occlusion recovery, unlocking new possibilities in downstream editing and creative applications.
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            <a href="https://www.alphaxiv.org/abs/2511.21692v1" target="_blank" rel="noopener noreferrer">
                重新审视跨难度级别的泛化：这并不简单
            </a>
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            Revisiting Generalization Across Difficulty Levels: It's Not So Easy
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yeganeh Kordi, Nihal V. Nayak, Max Zuo, Ilana Nguyen, Stephen H. Bach
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题表明其关注模型泛化能力与任务难度级别的关系，这属于机器学习通用理论范畴。没有明确的技术路径或应用场景指向推荐系统、搜索或广告领域。标题缺乏对Transformer架构、LLM技术或异构数据建模的具体提及，无法识别出在目标领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21692v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21692v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.AI</span></div>
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                    We investigate how well large language models (LLMs) generalize across different task difficulties, a key question for effective data curation and evaluation. Existing research is mixed regarding whether training on easier or harder data leads to better results, and whether those gains come on easier or harder test data. We address this question by conducting a systematic evaluation of LLMs' generalization across models, datasets, and fine-grained groups of example difficulty. We rank examples in six datasets using the outputs of thousands of different LLMs and Item Response Theory (IRT), a well-established difficulty metric in educational testing. Unlike prior work, our difficulty ratings are therefore determined solely by the abilities of many different LLMs, excluding human opinions of difficulty. With a more objective, larger-scale, and finer-grained analysis, we show that cross-difficulty generalization is often limited; training on either easy or hard data cannot achieve consistent improvements across the full range of difficulties. These results show the importance of having a range of difficulties in both training and evaluation data for LLMs, and that taking shortcuts with respect to difficulty is risky.
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            <a href="https://www.alphaxiv.org/abs/2511.21629v1" target="_blank" rel="noopener noreferrer">
                作者已死，但若其从未存在过呢？一项关于捷克AI与人类创作诗歌的接受度实验
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            The author is dead, but what if they never lived? A reception experiment on Czech AI- and human-authored poetry
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Anna Marklová, Ondřej Vinš, Martina Vokáčová, Jiří Milička
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究AI生成诗歌与人类创作诗歌的接受度比较实验，属于纯粹的AIGC（人工智能生成内容）和内容生成领域。这与我的核心关注点（推荐系统、搜索、广告中的技术进展及LLM应用）完全无关，且明确属于需要排除的“AIGC、内容生成”等无关主题范畴。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:53:59
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21629v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21629v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                    Large language models are increasingly capable of producing creative texts, yet most studies on AI-generated poetry focus on English -- a language that dominates training data. In this paper, we examine the perception of AI- and human-written Czech poetry. We ask if Czech native speakers are able to identify it and how they aesthetically judge it. Participants performed at chance level when guessing authorship (45.8\% correct on average), indicating that Czech AI-generated poems were largely indistinguishable from human-written ones. Aesthetic evaluations revealed a strong authorship bias: when participants believed a poem was AI-generated, they rated it as less favorably, even though AI poems were in fact rated equally or more favorably than human ones on average. The logistic regression model uncovered that the more the people liked a poem, the less probable was that they accurately assign the authorship. Familiarity with poetry or literary background had no effect on recognition accuracy. Our findings show that AI can convincingly produce poetry even in a morphologically complex, low-resource (with respect of the training data of AI models) Slavic language such as Czech. The results suggest that readers' beliefs about authorship and the aesthetic evaluation of the poem are interconnected.
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            <a href="https://www.alphaxiv.org/abs/2511.21533v1" target="_blank" rel="noopener noreferrer">
                孟加拉手语翻译：数据集创建挑战、基准测试与前景展望
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            Bangla Sign Language Translation: Dataset Creation Challenges, Benchmarking and Prospects
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Husne Ara Rubaiyeat, Hasan Mahmud, Md Kamrul Hasan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于特定语言（孟加拉语）的手语翻译，属于计算机视觉和特定领域应用的范畴。这与搜索、推荐或广告系统的核心进展、LLM技术、Transformer架构改进或异构数据统一建模均无直接关联。手语翻译主要涉及视觉理解和特定领域应用，不在当前关注的任何技术领域内。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:00:38
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21533v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21533v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Bangla Sign Language Translation (BdSLT) has been severely constrained so far as the language itself is very low resource. Standard sentence level dataset creation for BdSLT is of immense importance for developing AI based assistive tools for deaf and hard of hearing people of Bangla speaking community. In this paper, we present a dataset, IsharaKhobor , and two subset of it for enabling research. We also present the challenges towards developing the dataset and present some way forward by benchmarking with landmark based raw and RQE embedding. We do some ablation on vocabulary restriction and canonicalization of the same within the dataset, which resulted in two more datasets, IsharaKhobor_small and IsharaKhobor_canonical_small. The dataset is publicly available at: www.kaggle.com/datasets/hasanssl/isharakhobor [1].
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            <a href="https://www.alphaxiv.org/abs/2511.21398v1" target="_blank" rel="noopener noreferrer">
                Prune4Web：面向网页智能体的DOM树剪枝编程
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            Prune4Web: DOM Tree Pruning Programming for Web Agent
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiayuan Zhang, Kaiquan Chen, Zhihao Lu, Enshen Zhou, Qian Yu, Jing Zhang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于网页DOM树剪枝技术，属于网页自动化和智能体交互领域，与推荐系统、搜索或广告的核心技术无直接关联。DOM树剪枝主要用于网页结构优化和自动化任务，无法应用于推荐算法、搜索排序或广告投放等核心业务场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:49:39
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21398v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21398v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.HC</span><span class="category-tag">cs.MA</span></div>
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                    Web automation employs intelligent agents to execute high-level tasks by mimicking human interactions with web interfaces. Despite the capabilities of recent Large Language Model (LLM)-based web agents, navigating complex, real-world webpages efficiently remains a significant hurdle due to the prohibitively large size of Document Object Model (DOM) structures, often ranging from 10,000 to 100,000 tokens. Existing strategies typically rely on crude DOM truncation -- risking the loss of critical information -- or employ inefficient heuristics and separate ranking models, failing to achieve an optimal balance between precision and scalability. To address these challenges, we introduce Prune4Web, a novel paradigm that shifts DOM processing from resource-intensive LLM reading to efficient programmatic pruning. Central to our approach is DOM Tree Pruning Programming, where an LLM generates executable Python scoring scripts to dynamically filter DOM elements based on semantic cues from decomposed sub-tasks. This mechanism eliminates the need for LLMs to ingest raw, massive DOMs, instead delegating traversal and scoring to lightweight, interpretable programs. This methodology achieves a 25x to 50x reduction in candidate elements for grounding, thereby facilitating precise action localization while mitigating attention dilution. Furthermore, we propose a specialized data annotation pipeline and a two-turn dialogue training strategy that jointly optimizes the Planner, Programmatic Filter, and Grounder within a unified framework. Extensive experiments demonstrate state-of-the-art performance. Notably, on our low-level grounding task, Prune4Web dramatically improves accuracy from 46.8% to 88.28%, underscoring its efficacy in real-world web automation.
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            <a href="https://www.alphaxiv.org/abs/2511.21322v1" target="_blank" rel="noopener noreferrer">
                TALES：LLM生成故事中文化表征的分类学与分析
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            TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kirti Bhagat, Shaily Bhatt, Athul Velagapudi, Aditya Vashistha, Shachi Dave, Dan...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于LLM生成故事中的文化表征分析，属于纯粹的LLM内容生成研究范畴。虽然涉及LLM技术，但其应用场景（故事生成）和核心问题（文化表征）与推荐系统、搜索或广告领域没有任何直接或潜在的关联，完全超出了您关注的技术应用范围。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:07:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21322v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21322v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CL</span><span class="category-tag">cs.CY</span></div>
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                    Millions of users across the globe turn to AI chatbots for their creative needs, inviting widespread interest in understanding how such chatbots represent diverse cultures. At the same time, evaluating cultural representations in open-ended tasks remains challenging and underexplored. In this work, we present TALES, an evaluation of cultural misrepresentations in LLM-generated stories for diverse Indian cultural identities. First, we develop TALES-Tax, a taxonomy of cultural misrepresentations by collating insights from participants with lived experiences in India through focus groups (N=9) and individual surveys (N=15). Using TALES-Tax, we evaluate 6 models through a large-scale annotation study spanning 2,925 annotations from 108 annotators with lived cultural experience from across 71 regions in India and 14 languages. Concerningly, we find that 88\% of the generated stories contain one or more cultural inaccuracies, and such errors are more prevalent in mid- and low-resourced languages and stories based in peri-urban regions in India. Lastly, we transform the annotations into TALES-QA, a standalone question bank to evaluate the cultural knowledge of foundational models. Through this evaluation, we surprisingly discover that models often possess the requisite cultural knowledge despite generating stories rife with cultural misrepresentations.
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                为伊桑语开发开放式对话语音语料库
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            Developing an Open Conversational Speech Corpus for the Isan Language
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Adisai Na-Thalang, Chanakan Wittayasakpan, Kritsadha Phatcharoen, Supakit Buakaw
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于为特定语言（伊桑语）创建语音语料库，这属于语音处理领域。论文内容与推荐系统、搜索或广告的核心技术进展、LLM基础技术、Transformer架构改进或直接应用均无关联。语音语料库建设是语言资源开发工作，不涉及任何与推荐/搜索/广告相关的技术要素。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:57:31
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21229v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21229v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CL</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper introduces the development of the first open conversational speech dataset for the Isan language, the most widely spoken regional dialect in Thailand. Unlike existing speech corpora that are primarily based on read or scripted speech, this dataset consists of natural speech, thereby capturing authentic linguistic phenomena such as colloquials, spontaneous prosody, disfluencies, and frequent code-switching with central Thai. A key challenge in building this resource lies in the lack of a standardized orthography for Isan. Current writing practices vary considerably, due to the different lexical tones between Thai and Isan. This variability complicates the design of transcription guidelines and poses questions regarding consistency, usability, and linguistic authenticity. To address these issues, we establish practical transcription protocols that balance the need for representational accuracy with the requirements of computational processing. By releasing this dataset as an open resource, we aim to contribute to inclusive AI development, support research on underrepresented languages, and provide a basis for addressing the linguistic and technical challenges inherent in modeling conversational speech.
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21140v1" target="_blank" rel="noopener noreferrer">
                如何正确报告LLM作为评估者的评估结果
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            How to Correctly Report LLM-as-a-Judge Evaluations
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chungpa Lee, Thomas Zeng, Jongwon Jeong, Jy-yong Sohn, Kangwook Lee
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及LLM评估方法和报告标准，这属于纯粹的评估基准和NLP中心话题，与您关注的推荐系统、搜索或广告的核心技术进展无关。论文内容似乎专注于LLM评估的元研究，没有展示在推荐、搜索或广告领域的潜在应用价值。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:46:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21140v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21140v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CL</span><span class="category-tag">stat.AP</span><span class="category-tag">stat.ML</span></div>
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                    Large language models (LLMs) are increasingly used as evaluators in lieu of humans. While scalable, their judgments are noisy due to imperfect specificity and sensitivity of LLMs, leading to biased accuracy estimates. Although bias-correction methods exist, they are underutilized in LLM research and typically assume exact knowledge of the model's specificity and sensitivity. Furthermore, in general we only have estimates of these values and it is not well known how to properly construct confidence intervals using only estimates. This work presents a simple plug-in framework that corrects such bias and constructs confidence intervals reflecting uncertainty from both test and calibration dataset, enabling practical and statistically sound LLM-based evaluation. Additionally, to reduce uncertainty in the accuracy estimate, we introduce an adaptive algorithm that efficiently allocates calibration sample sizes.
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            <a href="https://www.alphaxiv.org/abs/2511.21060v1" target="_blank" rel="noopener noreferrer">
                两阶段符号过程中的Zipf分布：随机词汇过滤下的稳定性
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        <div class="mb-2 text-base text-gray-700">
            Zipf Distributions from Two-Stage Symbolic Processes: Stability Under Stochastic Lexical Filtering
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Vladimir Berman
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文研究Zipf分布和符号过程的数学性质，属于理论统计语言学范畴。虽然Zipf定律在信息检索中有历史应用，但本文专注于随机词汇过滤下的稳定性分析，没有明确的技术应用指向。该研究缺乏与推荐系统、搜索或广告的直接关联，也不涉及LLM、Transformer架构或异构数据建模等当前关注领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:59:40
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21060v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21060v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">stat.ME</span><span class="category-tag">cs.CL</span><span class="category-tag">stat.ML</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Zipf's law in language lacks a definitive origin, debated across fields. This study explains Zipf-like behavior using geometric mechanisms without linguistic elements. The Full Combinatorial Word Model (FCWM) forms words from a finite alphabet, generating a geometric distribution of word lengths. Interacting exponential forces yield a power-law rank-frequency curve, determined by alphabet size and blank symbol probability. Simulations support predictions, matching English, Russian, and mixed-genre data. The symbolic model suggests Zipf-type laws arise from geometric constraints, not communicative efficiency.
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            <a href="https://www.alphaxiv.org/abs/2511.21691v1" target="_blank" rel="noopener noreferrer">
                画布到图像：基于多模态控制的组合式图像生成
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            Canvas-to-Image: Compositional Image Generation with Multimodal Controls
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yusuf Dalva, Guocheng Gordon Qian, Maya Goldenberg, Tsai-Shien Chen, Kfir Aberma...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于组合式图像生成技术，属于纯粹的AIGC和内容生成领域。虽然涉及多模态控制，但核心是图像生成而非推荐、搜索或广告中的排名或理解任务，与当前关注的RecSys/Search/Ads核心进展、使能技术或直接应用无关。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:56
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21691v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21691v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    While modern diffusion models excel at generating high-quality and diverse images, they still struggle with high-fidelity compositional and multimodal control, particularly when users simultaneously specify text prompts, subject references, spatial arrangements, pose constraints, and layout annotations. We introduce Canvas-to-Image, a unified framework that consolidates these heterogeneous controls into a single canvas interface, enabling users to generate images that faithfully reflect their intent. Our key idea is to encode diverse control signals into a single composite canvas image that the model can directly interpret for integrated visual-spatial reasoning. We further curate a suite of multi-task datasets and propose a Multi-Task Canvas Training strategy that optimizes the diffusion model to jointly understand and integrate heterogeneous controls into text-to-image generation within a unified learning paradigm. This joint training enables Canvas-to-Image to reason across multiple control modalities rather than relying on task-specific heuristics, and it generalizes well to multi-control scenarios during inference. Extensive experiments show that Canvas-to-Image significantly outperforms state-of-the-art methods in identity preservation and control adherence across challenging benchmarks, including multi-person composition, pose-controlled composition, layout-constrained generation, and multi-control generation.
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            <a href="https://www.alphaxiv.org/abs/2511.21690v1" target="_blank" rel="noopener noreferrer">
                TraceGen：在3D轨迹空间中构建世界模型，实现跨具身视频的学习
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            TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Seungjae Lee, Yoonkyo Jung, Inkook Chun, Yao-Chih Lee, Zikui Cai, Hongjia Huang,...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D轨迹空间中的世界建模和跨具身视频学习，属于计算机视觉和机器人领域。虽然提到了世界建模和学习，但其核心是3D轨迹和具身智能，与搜索、推荐、广告系统没有直接关联，也不涉及Transformer架构改进或LLM技术在这些领域的应用。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:59:55
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21690v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21690v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Learning new robot tasks on new platforms and in new scenes from only a handful of demonstrations remains challenging. While videos of other embodiments - humans and different robots - are abundant, differences in embodiment, camera, and environment hinder their direct use. We address the small-data problem by introducing a unifying, symbolic representation - a compact 3D "trace-space" of scene-level trajectories - that enables learning from cross-embodiment, cross-environment, and cross-task videos. We present TraceGen, a world model that predicts future motion in trace-space rather than pixel space, abstracting away appearance while retaining the geometric structure needed for manipulation. To train TraceGen at scale, we develop TraceForge, a data pipeline that transforms heterogeneous human and robot videos into consistent 3D traces, yielding a corpus of 123K videos and 1.8M observation-trace-language triplets. Pretraining on this corpus produces a transferable 3D motion prior that adapts efficiently: with just five target robot videos, TraceGen attains 80% success across four tasks while offering 50-600x faster inference than state-of-the-art video-based world models. In the more challenging case where only five uncalibrated human demonstration videos captured on a handheld phone are available, it still reaches 67.5% success on a real robot, highlighting TraceGen's ability to adapt across embodiments without relying on object detectors or heavy pixel-space generation.
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            <a href="https://www.alphaxiv.org/abs/2511.21681v1" target="_blank" rel="noopener noreferrer">
                无像素视觉：基于相机轨迹的感知
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            Seeing without Pixels: Perception from Camera Trajectories
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zihui Xue, Kristen Grauman, Dima Damen, Andrew Zisserman, Tengda Han
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文关注从相机轨迹而非像素数据进行感知，这属于纯粹的计算机视觉研究领域。该主题与推荐系统、搜索或广告的核心技术焦点没有直接关联，也没有展示出在这些领域应用的潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:57:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21681v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21681v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Can one perceive a video's content without seeing its pixels, just from the camera trajectory-the path it carves through space? This paper is the first to systematically investigate this seemingly implausible question. Towards this end, we propose a contrastive learning framework to train CamFormer, a dedicated encoder that projects camera pose trajectories into a joint embedding space, aligning them with natural language. We find that, contrary to its apparent simplicity, the camera trajectory is a remarkably informative signal to uncover video content. In other words, "how you move" can indeed reveal "what you are doing" (egocentric) or "observing" (exocentric). We demonstrate the versatility of our learned CamFormer embeddings on a diverse suite of downstream tasks, ranging from cross-modal alignment to classification and temporal analysis. Importantly, our representations are robust across diverse camera pose estimation methods, including both high-fidelity multi-sensored and standard RGB-only estimators. Our findings establish camera trajectory as a lightweight, robust, and versatile modality for perceiving video content.
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            <a href="https://www.alphaxiv.org/abs/2511.21673v1" target="_blank" rel="noopener noreferrer">
                利用3D MRI引导的混合深度学习模型革新胶质瘤分割与分级
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            Revolutionizing Glioma Segmentation & Grading Using 3D MRI - Guided Hybrid Deep Learning Models
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pandiyaraju V, Sreya Mynampati, Abishek Karthik, Poovarasan L, D. Saraswathi
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学影像分析中的脑胶质瘤分割与分级，属于明确的医疗领域应用。标题中提到的3D MRI、胶质瘤分割等技术内容与推荐系统、搜索、广告等核心关注领域完全无关，且没有显示出任何在异构数据处理或Transformer架构方面的通用技术潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:51:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21673v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21673v1
                </a>
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Gliomas are brain tumor types that have a high mortality rate which means early and accurate diagnosis is important for therapeutic intervention for the tumors. To address this difficulty, the proposed research will develop a hybrid deep learning model which integrates U-Net based segmentation and a hybrid DenseNet-VGG classification network with multihead attention and spatial-channel attention capabilities. The segmentation model will precisely demarcate the tumors in a 3D volume of MRI data guided by spatial and contextual information. The classification network which combines a branch of both DenseNet and VGG, will incorporate the demarcated tumor on which features with attention mechanisms would be focused on clinically relevant features. High-dimensional 3D MRI data could successfully be utilized in the model through preprocessing steps which are normalization, resampling, and data augmentation. Through a variety of measures the framework is evaluated: measures of performance in segmentation are Dice coefficient and Mean Intersection over Union (IoU) and measures of performance in classification are accuracy precision, recall, and F1-score. The hybrid framework that has been proposed has demonstrated through physical testing that it has the capability of obtaining a Dice coefficient of 98% in tumor segmentation, and 99% on classification accuracy, outperforming traditional CNN models and attention-free methods. Utilizing multi-head attention mechanisms enhances notions of priority in aspects of the tumor that are clinically significant, and enhances interpretability and accuracy. The results suggest a great potential of the framework in facilitating the timely and reliable diagnosis and grading of glioma by clinicians is promising, allowing for better planning of patient treatment.
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            <a href="https://www.alphaxiv.org/abs/2511.21653v1" target="_blank" rel="noopener noreferrer">
                CaFlow：通过因果反事实流增强长期动作质量评估
            </a>
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            CaFlow: Enhancing Long-Term Action Quality Assessment with Causal Counterfactual Flow
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ruisheng Han, Kanglei Zhou, Shuang Chen, Amir Atapour-Abarghouei, Hubert P. H. S...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于动作质量评估，属于计算机视觉中的动作识别领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然提到了因果推理方法，但其应用场景（长期动作质量评估）与文本中明确的非相关主题（纯视觉论文）高度重叠，缺乏在推荐、搜索或广告领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 18:25:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21653v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21653v1
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                    Action Quality Assessment (AQA) predicts fine-grained execution scores from action videos and is widely applied in sports, rehabilitation, and skill evaluation. Long-term AQA, as in figure skating or rhythmic gymnastics, is especially challenging since it requires modeling extended temporal dynamics while remaining robust to contextual confounders. Existing approaches either depend on costly annotations or rely on unidirectional temporal modeling, making them vulnerable to spurious correlations and unstable long-term representations. To this end, we propose CaFlow, a unified framework that integrates counterfactual de-confounding with bidirectional time-conditioned flow. The Causal Counterfactual Regularization (CCR) module disentangles causal and confounding features in a self-supervised manner and enforces causal robustness through counterfactual interventions, while the BiT-Flow module models forward and backward dynamics with a cycle-consistency constraint to produce smoother and more coherent representations. Extensive experiments on multiple long-term AQA benchmarks demonstrate that CaFlow achieves state-of-the-art performance. Code is available at https://github.com/Harrison21/CaFlow
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            <a href="https://www.alphaxiv.org/abs/2511.21606v1" target="_blank" rel="noopener noreferrer">
                ReSAM：精炼、重查询与强化：面向遥感图像的点监督分割的自提示方法
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            ReSAM: Refine, Requery, and Reinforce: Self-Prompting Point-Supervised Segmentation for Remote Sensing Images
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>M. Naseer Subhani
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像分割这一计算机视觉领域，与推荐系统、搜索或广告的核心技术无直接关联。虽然标题中提到的“自提示”概念在LLM中有所应用，但该论文将其应用于特定领域的图像分割任务，缺乏在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:26:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21606v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21606v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Interactive segmentation models such as the Segment Anything Model (SAM) have demonstrated remarkable generalization on natural images, but perform suboptimally on remote sensing imagery (RSI) due to severe domain shift and the scarcity of dense annotations. To address this, we propose a self-prompting, point-supervised framework that adapts SAM to RSIs using only sparse point annotations. Our method employs a Refine-Requery-Reinforce loop, where coarse pseudo-masks are generated from initial points (Refine), improved with self-constructed box prompts (Requery), and embeddings are aligned across iterations to reduce confirmation bias (Reinforce). Without relying on full-mask supervision, our approach progressively enhances SAM's segmentation quality and domain robustness through self-guided prompt adaptation . We evaluate our proposed method on three RSI benchmark datasets, including WHU, HRSID, and NWPU VHR-10, showing that our method consistently surpasses pretrained SAM and recent point-supervised segmentation methods. Our results demonstrate that self-prompting and semantic alignment provide an efficient path towards scalable, point-level adaptation of foundation segmentation models for remote sensing applications.
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            <a href="https://www.alphaxiv.org/abs/2511.21592v1" target="_blank" rel="noopener noreferrer">
                MoGAN：通过少步运动对抗后训练改进视频扩散中的运动质量
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            MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Haotian Xue, Qi Chen, Zhonghao Wang, Xun Huang, Eli Shechtman, Jinrong Xie, Yong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于视频生成中的运动质量改进，属于纯粹的视觉内容生成领域。虽然提到了扩散模型和对抗训练技术，但这些技术应用仅限于视频生成，与推荐系统、搜索或广告的排名和匹配任务没有直接关联。论文内容属于被明确排除的'纯粹视觉'和'AIGC/内容生成'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 17:09:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21592v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21592v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics. A key limitation is that the standard denoising MSE objective provides no direct supervision on temporal consistency, allowing models to achieve low loss while still generating poor motion. We propose MoGAN, a motion-centric post-training framework that improves motion realism without reward models or human preference data. Built atop a 3-step distilled video diffusion model, we train a DiT-based optical-flow discriminator to differentiate real from generated motion, combined with a distribution-matching regularizer to preserve visual fidelity. With experiments on Wan2.1-T2V-1.3B, MoGAN substantially improves motion quality across benchmarks. On VBench, MoGAN boosts motion score by +7.3% over the 50-step teacher and +13.3% over the 3-step DMD model. On VideoJAM-Bench, MoGAN improves motion score by +7.4% over the teacher and +8.8% over DMD, while maintaining comparable or even better aesthetic and image-quality scores. A human study further confirms that MoGAN is preferred for motion quality (52% vs. 38% for the teacher; 56% vs. 29% for DMD). Overall, MoGAN delivers significantly more realistic motion without sacrificing visual fidelity or efficiency, offering a practical path toward fast, high-quality video generation. Project webpage is: https://xavihart.github.io/mogan.
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            <a href="https://www.alphaxiv.org/abs/2511.21582v1" target="_blank" rel="noopener noreferrer">
                基于深度学习的口腔病变多分类与分层数据增强
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            Deep Learning-Based Multiclass Classification of Oral Lesions with Stratified Augmentation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Joy Naoum, Revana Salama, Ali Hamdi
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医疗领域的口腔病变分类，属于明确的医学应用场景。尽管使用了深度学习技术，但内容完全属于医疗/生物学领域，与推荐系统、搜索或广告没有任何关联。该论文不涉及任何与推荐、搜索或广告相关的技术或应用场景。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:56:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21582v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21582v1
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                    Oral cancer is highly common across the globe and is mostly diagnosed during the later stages due to the close visual similarity to benign, precancerous, and malignant lesions in the oral cavity. Implementing computer aided diagnosis systems early on has the potential to greatly improve clinical outcomes. This research intends to use deep learning to build a multiclass classifier for sixteen different oral lesions. To overcome the challenges of limited and imbalanced datasets, the proposed technique combines stratified data splitting and advanced data augmentation and oversampling to perform the classification. The experimental results, which achieved 83.33 percent accuracy, 89.12 percent precision, and 77.31 percent recall, demonstrate the superiority of the suggested model over state of the art methods now in use. The suggested model effectively conveys the effectiveness of oversampling and augmentation strategies in situations where the minority class classification performance is noteworthy. As a first step toward trustworthy computer aided diagnostic systems for the early detection of oral cancer in clinical settings, the suggested framework shows promise.
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                Harmony：通过跨任务协同实现音频与视频生成的和谐统一
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            Harmony: Harmonizing Audio and Video Generation through Cross-Task Synergy
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Teng Hu, Zhentao Yu, Guozhen Zhang, Zihan Su, Zhengguang Zhou, Youliang Zhang, Y...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频和视频生成的跨模态协同，属于纯粹的视觉和语音生成领域，与推荐系统、搜索或广告的核心技术无关。论文内容涉及AIGC和内容生成，这些都属于明确排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:53:05
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21579v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21579v1
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                    The synthesis of synchronized audio-visual content is a key challenge in generative AI, with open-source models facing challenges in robust audio-video alignment. Our analysis reveals that this issue is rooted in three fundamental challenges of the joint diffusion process: (1) Correspondence Drift, where concurrently evolving noisy latents impede stable learning of alignment; (2) inefficient global attention mechanisms that fail to capture fine-grained temporal cues; and (3) the intra-modal bias of conventional Classifier-Free Guidance (CFG), which enhances conditionality but not cross-modal synchronization. To overcome these challenges, we introduce Harmony, a novel framework that mechanistically enforces audio-visual synchronization. We first propose a Cross-Task Synergy training paradigm to mitigate drift by leveraging strong supervisory signals from audio-driven video and video-driven audio generation tasks. Then, we design a Global-Local Decoupled Interaction Module for efficient and precise temporal-style alignment. Finally, we present a novel Synchronization-Enhanced CFG (SyncCFG) that explicitly isolates and amplifies the alignment signal during inference. Extensive experiments demonstrate that Harmony establishes a new state-of-the-art, significantly outperforming existing methods in both generation fidelity and, critically, in achieving fine-grained audio-visual synchronization.
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            <a href="https://www.alphaxiv.org/abs/2511.21575v1" target="_blank" rel="noopener noreferrer">
                使用2D/3D配准损失增强骨盆荧光透视中的标志点检测模型
            </a>
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            <i class="fa fa-star mr-1"></i>1/10
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    <div class="paper-details">
        <div class="mb-2 text-base text-gray-700">
            Enhanced Landmark Detection Model in Pelvic Fluoroscopy using 2D/3D Registration Loss
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chou Mo, Yehyun Suh, J. Ryan Martin, Daniel Moyer
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学影像领域的骨盆荧光透视和标志点检测，属于医学/生物学特定应用范畴。该技术主要针对医疗诊断和手术导航，与推荐系统、搜索或广告领域没有任何直接或间接的关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:50:06
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21575v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21575v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Automated landmark detection offers an efficient approach for medical professionals to understand patient anatomic structure and positioning using intra-operative imaging. While current detection methods for pelvic fluoroscopy demonstrate promising accuracy, most assume a fixed Antero-Posterior view of the pelvis. However, orientation often deviates from this standard view, either due to repositioning of the imaging unit or of the target structure itself. To address this limitation, we propose a novel framework that incorporates 2D/3D landmark registration into the training of a U-Net landmark prediction model. We analyze the performance difference by comparing landmark detection accuracy between the baseline U-Net, U-Net trained with Pose Estimation Loss, and U-Net fine-tuned with Pose Estimation Loss under realistic intra-operative conditions where patient pose is variable.
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            <a href="https://www.alphaxiv.org/abs/2511.21565v1" target="_blank" rel="noopener noreferrer">
                UAVLight：无人机场景中光照鲁棒三维重建的基准
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            UAVLight: A Benchmark for Illumination-Robust 3D Reconstruction in Unmanned Aerial Vehicle (UAV) Scenes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kang Du, Xue Liao, Junpeng Xia, Chaozheng Guo, Yi Gu, Yirui Guan, Duotun Wang, S...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于无人机场景中的3D视觉重建和光照鲁棒性基准测试，属于纯粹的计算机视觉领域。虽然提到了3D重建，但没有任何与推荐系统、搜索或广告相关的技术内容或潜在应用。该研究完全属于被排除的'纯粹视觉'类别，与当前关注的核心领域毫无关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 16:38:29
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21565v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21565v1
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                    Illumination inconsistency is a fundamental challenge in multi-view 3D reconstruction. Variations in sunlight direction, cloud cover, and shadows break the constant-lighting assumption underlying both classical multi-view stereo (MVS) and structure from motion (SfM) pipelines and recent neural rendering methods, leading to geometry drift, color inconsistency, and shadow imprinting. This issue is especially critical in UAV-based reconstruction, where long flight durations and outdoor environments make lighting changes unavoidable. However, existing datasets either restrict capture to short time windows, thus lacking meaningful illumination diversity, or span months and seasons, where geometric and semantic changes confound the isolated study of lighting robustness. We introduce UAVLight, a controlled-yet-real benchmark for illumination-robust 3D reconstruction. Each scene is captured along repeatable, geo-referenced flight paths at multiple fixed times of day, producing natural lighting variation under consistent geometry, calibration, and viewpoints. With standardized evaluation protocols across lighting conditions, UAVLight provides a reliable foundation for developing and benchmarking reconstruction methods that are consistent, faithful, and relightable in real outdoor environments.
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            <a href="https://www.alphaxiv.org/abs/2511.21530v1" target="_blank" rel="noopener noreferrer">
                非均匀时间跨度下具有特征约束的年龄特异性阿尔茨海默病预测
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            The Age-specific Alzheimer 's Disease Prediction with Characteristic Constraints in Nonuniform Time Span
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Hong, Kaifeng Huang
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的阿尔茨海默病预测，属于明确的生物医学应用范畴。根据用户指定的无关主题列表，医学、生物学等特定领域应用应被排除，且该研究没有任何与推荐系统、搜索或广告相关的技术内容或潜在应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:58:44
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21530v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21530v1
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                    Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.
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            <a href="https://www.alphaxiv.org/abs/2511.21519v1" target="_blank" rel="noopener noreferrer">
                抗核抗体图像的自适应学习
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            Self-Paced Learning for Images of Antinuclear Antibodies
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yiyang Jiang, Guangwu Qian, Jiaxin Wu, Qi Huang, Qing Li, Yongkang Wu, Xiao-Yong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确涉及医学领域的抗核抗体图像分析，这属于明确的无关主题范畴。标题中提到的自适应学习虽然是通用机器学习技术，但应用于特定医学诊断任务，与推荐系统、搜索或广告领域没有任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:50:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21519v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21519v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Antinuclear antibody (ANA) testing is a crucial method for diagnosing autoimmune disorders, including lupus, Sjögren's syndrome, and scleroderma. Despite its importance, manual ANA detection is slow, labor-intensive, and demands years of training. ANA detection is complicated by over 100 coexisting antibody types, resulting in vast fluorescent pattern combinations. Although machine learning and deep learning have enabled automation, ANA detection in real-world clinical settings presents unique challenges as it involves multi-instance, multi-label (MIML) learning. In this paper, a novel framework for ANA detection is proposed that handles the complexities of MIML tasks using unaltered microscope images without manual preprocessing. Inspired by human labeling logic, it identifies consistent ANA sub-regions and assigns aggregated labels accordingly. These steps are implemented using three task-specific components: an instance sampler, a probabilistic pseudo-label dispatcher, and self-paced weight learning rate coefficients. The instance sampler suppresses low-confidence instances by modeling pattern confidence, while the dispatcher adaptively assigns labels based on instance distinguishability. Self-paced learning adjusts training according to empirical label observations. Our framework overcomes limitations of traditional MIML methods and supports end-to-end optimization. Extensive experiments on one ANA dataset and three public medical MIML benchmarks demonstrate the superiority of our framework. On the ANA dataset, our model achieves up to +7.0% F1-Macro and +12.6% mAP gains over the best prior method, setting new state-of-the-art results. It also ranks top-2 across all key metrics on public datasets, reducing Hamming loss and one-error by up to 18.2% and 26.9%, respectively. The source code can be accessed at https://github.com/fletcherjiang/ANA-SelfPacedLearning.
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            <a href="https://www.alphaxiv.org/abs/2511.21507v1" target="_blank" rel="noopener noreferrer">
                深度伪造检测器的通用设计选择
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            Generalized Design Choices for Deepfake Detectors
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Lorenzo Pellegrini, Serafino Pandolfini, Davide Maltoni, Matteo Ferrara, Marco P...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于深度伪造检测技术，这属于内容安全验证领域，与推荐系统、搜索或广告的核心技术进展无关。论文主题涉及内容真实性验证，而非排名、检索或用户行为建模等核心领域，因此与当前关注点完全不相关。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:40:58
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                <a href="https://arxiv.org/abs/2511.21507v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21507v1
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                    The effectiveness of deepfake detection methods often depends less on their core design and more on implementation details such as data preprocessing, augmentation strategies, and optimization techniques. These factors make it difficult to fairly compare detectors and to understand which factors truly contribute to their performance. To address this, we systematically investigate how different design choices influence the accuracy and generalization capabilities of deepfake detection models, focusing on aspects related to training, inference, and incremental updates. By isolating the impact of individual factors, we aim to establish robust, architecture-agnostic best practices for the design and development of future deepfake detection systems. Our experiments identify a set of design choices that consistently improve deepfake detection and enable state-of-the-art performance on the AI-GenBench benchmark.
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                MobileI2V：移动设备上的快速高分辨率图像到视频生成
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            MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuai Zhang, Bao Tang, Siyuan Yu, Yueting Zhu, Jingfeng Yao, Ya Zou, Shanglin Yu...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于移动设备上的图像到视频生成技术，这属于纯粹的视觉内容生成领域。虽然标题提到移动设备优化，但核心是视觉模态的生成任务，与推荐系统、搜索或广告的排名和建模需求没有直接关联，也不涉及LLM或Transformer架构在推荐领域的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 15:09:02
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21475v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21475v1
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                    Recently, video generation has witnessed rapid advancements, drawing increasing attention to image-to-video (I2V) synthesis on mobile devices. However, the substantial computational complexity and slow generation speed of diffusion models pose significant challenges for real-time, high-resolution video generation on resource-constrained mobile devices. In this work, we propose MobileI2V, a 270M lightweight diffusion model for real-time image-to-video generation on mobile devices. The core lies in: (1) We analyzed the performance of linear attention modules and softmax attention modules on mobile devices, and proposed a linear hybrid architecture denoiser that balances generation efficiency and quality. (2) We design a time-step distillation strategy that compresses the I2V sampling steps from more than 20 to only two without significant quality loss, resulting in a 10-fold increase in generation speed. (3) We apply mobile-specific attention optimizations that yield a 2-fold speed-up for attention operations during on-device inference. MobileI2V enables, for the first time, fast 720p image-to-video generation on mobile devices, with quality comparable to existing models. Under one-step conditions, the generation speed of each frame of 720p video is less than 100 ms. Our code is available at: https://github.com/hustvl/MobileI2V.
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            <a href="https://www.alphaxiv.org/abs/2511.21420v1" target="_blank" rel="noopener noreferrer">
                SAM引导的遥感变化描述中语义与运动变化区域挖掘
            </a>
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        <span class="score-badge bg-gray-100 text-gray-800">
            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            SAM Guided Semantic and Motion Changed Region Mining for Remote Sensing Change Captioning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Futian Wang, Mengqi Wang, Xiao Wang, Haowen Wang, Jin Tang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于遥感图像变化检测和描述，属于计算机视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。虽然涉及变化检测技术，但这种遥感应用场景与用户行为建模、内容排序或广告投放等商业系统需求相距甚远，无法找到在RecSys/Search/Ads领域的潜在应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 14:11:19
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21420v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21420v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Remote sensing change captioning is an emerging and popular research task that aims to describe, in natural language, the content of interest that has changed between two remote sensing images captured at different times. Existing methods typically employ CNNs/Transformers to extract visual representations from the given images or incorporate auxiliary tasks to enhance the final results, with weak region awareness and limited temporal alignment. To address these issues, this paper explores the use of the SAM (Segment Anything Model) foundation model to extract region-level representations and inject region-of-interest knowledge into the captioning framework. Specifically, we employ a CNN/Transformer model to extract global-level vision features, leverage the SAM foundation model to delineate semantic- and motion-level change regions, and utilize a specially constructed knowledge graph to provide information about objects of interest. These heterogeneous sources of information are then fused via cross-attention, and a Transformer decoder is used to generate the final natural language description of the observed changes. Extensive experimental results demonstrate that our method achieves state-of-the-art performance across multiple widely used benchmark datasets. The source code of this paper will be released on https://github.com/Event-AHU/SAM_ChangeCaptioning
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            <a href="https://www.alphaxiv.org/abs/2511.21367v1" target="_blank" rel="noopener noreferrer">
                Endo-G²T：用于内窥镜场景的几何引导与时间感知的时间嵌入4D高斯泼溅
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            Endo-G$^{2}$T: Geometry-Guided & Temporally Aware Time-Embedded 4DGS For Endoscopic Scenes
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yangle Liu, Fengze Li, Kan Liu, Jieming Ma
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于内窥镜场景的4D重建和建模，属于计算机视觉中的特定医疗应用领域。虽然涉及时间序列建模和几何引导技术，但这些技术主要针对医疗内窥镜这一特定场景，与推荐系统、搜索或广告的核心技术领域没有直接关联。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:12:21
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21367v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21367v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Endoscopic (endo) video exhibits strong view-dependent effects such as specularities, wet reflections, and occlusions. Pure photometric supervision misaligns with geometry and triggers early geometric drift, where erroneous shapes are reinforced during densification and become hard to correct. We ask how to anchor geometry early for 4D Gaussian splatting (4DGS) while maintaining temporal consistency and efficiency in dynamic endoscopic scenes. Thus, we present Endo-G$^{2}$T, a geometry-guided and temporally aware training scheme for time-embedded 4DGS. First, geo-guided prior distillation converts confidence-gated monocular depth into supervision with scale-invariant depth and depth-gradient losses, using a warm-up-to-cap schedule to inject priors softly and avoid early overfitting. Second, a time-embedded Gaussian field represents dynamics in XYZT with a rotor-like rotation parameterization, yielding temporally coherent geometry with lightweight regularization that favors smooth motion and crisp opacity boundaries. Third, keyframe-constrained streaming improves efficiency and long-horizon stability through keyframe-focused optimization under a max-points budget, while non-keyframes advance with lightweight updates. Across EndoNeRF and StereoMIS-P1 datasets, Endo-G$^{2}$T achieves state-of-the-art results among monocular reconstruction baselines.
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            <a href="https://www.alphaxiv.org/abs/2511.21365v1" target="_blank" rel="noopener noreferrer">
                PFF-Net：用于点云法向量估计的补丁特征拟合
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            PFF-Net: Patch Feature Fitting for Point Cloud Normal Estimation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qing Li, Huifang Feng, Kanle Shi, Yue Gao, Yi Fang, Yu-Shen Liu, Zhizhong Han
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于点云处理中的法向量估计，属于计算机视觉和3D视觉领域。虽然点云数据在某些特定场景下可能用于推荐或搜索（如3D产品推荐），但该论文的核心技术是纯粹的3D视觉任务，没有明确提及或暗示在推荐系统、搜索或广告中的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:12:14
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21365v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21365v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Estimating the normal of a point requires constructing a local patch to provide center-surrounding context, but determining the appropriate neighborhood size is difficult when dealing with different data or geometries. Existing methods commonly employ various parameter-heavy strategies to extract a full feature description from the input patch. However, they still have difficulties in accurately and efficiently predicting normals for various point clouds. In this work, we present a new idea of feature extraction for robust normal estimation of point clouds. We use the fusion of multi-scale features from different neighborhood sizes to address the issue of selecting reasonable patch sizes for various data or geometries. We seek to model a patch feature fitting (PFF) based on multi-scale features to approximate the optimal geometric description for normal estimation and implement the approximation process via multi-scale feature aggregation and cross-scale feature compensation. The feature aggregation module progressively aggregates the patch features of different scales to the center of the patch and shrinks the patch size by removing points far from the center. It not only enables the network to precisely capture the structure characteristic in a wide range, but also describes highly detailed geometries. The feature compensation module ensures the reusability of features from earlier layers of large scales and reveals associated information in different patch sizes. Our approximation strategy based on aggregating the features of multiple scales enables the model to achieve scale adaptation of varying local patches and deliver the optimal feature description. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both synthetic and real-world datasets with fewer network parameters and running time.
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            <a href="https://www.alphaxiv.org/abs/2511.21364v1" target="_blank" rel="noopener noreferrer">
                BanglaMM-Disaster：一种基于多模态Transformer的深度学习框架，用于孟加拉语多类别灾害分类
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            BanglaMM-Disaster: A Multimodal Transformer-Based Deep Learning Framework for Multiclass Disaster Classification in Bangla
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ariful Islam, Md Rifat Hossen, Md. Mahmudul Arif, Abdullah Al Noman, Md Arifur R...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于孟加拉语的灾害分类任务，属于特定领域应用，与推荐系统、搜索或广告的核心技术无关。多模态Transformer架构虽然技术上相关，但应用场景局限于灾害分类，没有展示在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 13:11:46
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21364v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21364v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Natural disasters remain a major challenge for Bangladesh, so real-time monitoring and quick response systems are essential. In this study, we present BanglaMM-Disaster, an end-to-end deep learning-based multimodal framework for disaster classification in Bangla, using both textual and visual data from social media. We constructed a new dataset of 5,037 Bangla social media posts, each consisting of a caption and a corresponding image, annotated into one of nine disaster-related categories. The proposed model integrates transformer-based text encoders, including BanglaBERT, mBERT, and XLM-RoBERTa, with CNN backbones such as ResNet50, DenseNet169, and MobileNetV2, to process the two modalities. Using early fusion, the best model achieves 83.76% accuracy. This surpasses the best text-only baseline by 3.84% and the image-only baseline by 16.91%. Our analysis also shows reduced misclassification across all classes, with noticeable improvements for ambiguous examples. This work fills a key gap in Bangla multimodal disaster analysis and demonstrates the benefits of combining multiple data types for real-time disaster response in low-resource settings.
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            <a href="https://www.alphaxiv.org/abs/2511.21339v1" target="_blank" rel="noopener noreferrer">
                SurgMLLMBench：用于手术场景理解的多模态大语言模型基准数据集
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            SurgMLLMBench: A Multimodal Large Language Model Benchmark Dataset for Surgical Scene Understanding
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Tae-Min Choi, Tae Kyeong Jeong, Garam Kim, Jaemin Lee, Yeongyoon Koh, In Cheul C...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于手术场景理解这一医疗领域特定应用，属于明确的无关主题。虽然涉及多模态大语言模型技术，但其应用场景与搜索、推荐或广告系统完全无关，无法为我的核心关注领域提供任何潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:44:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21339v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21339v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Recent advances in multimodal large language models (LLMs) have highlighted their potential for medical and surgical applications. However, existing surgical datasets predominantly adopt a Visual Question Answering (VQA) format with heterogeneous taxonomies and lack support for pixel-level segmentation, limiting consistent evaluation and applicability. We present SurgMLLMBench, a unified multimodal benchmark explicitly designed for developing and evaluating interactive multimodal LLMs for surgical scene understanding, including the newly collected Micro-surgical Artificial Vascular anastomosIS (MAVIS) dataset. It integrates pixel-level instrument segmentation masks and structured VQA annotations across laparoscopic, robot-assisted, and micro-surgical domains under a unified taxonomy, enabling comprehensive evaluation beyond traditional VQA tasks and richer visual-conversational interactions. Extensive baseline experiments show that a single model trained on SurgMLLMBench achieves consistent performance across domains and generalizes effectively to unseen datasets. SurgMLLMBench will be publicly released as a robust resource to advance multimodal surgical AI research, supporting reproducible evaluation and development of interactive surgical reasoning models.
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            <a href="https://www.alphaxiv.org/abs/2511.21337v1" target="_blank" rel="noopener noreferrer">
                用于交通流控基础设施高效异常检测的混合SIFT-SNN方法
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            Hybrid SIFT-SNN for Efficient Anomaly Detection of Traffic Flow-Control Infrastructure
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Munish Rathee, Boris Bačić, Maryam Doborjeh
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于交通基础设施的异常检测，属于特定领域应用，与推荐系统、搜索或广告的核心技术无关。虽然涉及高效算法，但缺乏与LLM、Transformer架构或推荐系统相关技术的任何关联，无法看出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 12:40:18
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21337v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21337v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    This paper presents the SIFT-SNN framework, a low-latency neuromorphic signal-processing pipeline for real-time detection of structural anomalies in transport infrastructure. The proposed approach integrates Scale-Invariant Feature Transform (SIFT) for spatial feature encoding with a latency-driven spike conversion layer and a Leaky Integrate-and-Fire (LIF) Spiking Neural Network (SNN) for classification. The Auckland Harbour Bridge dataset is recorded under various weather and lighting conditions, comprising 6,000 labelled frames that include both real and synthetically augmented unsafe cases. The presented system achieves a classification accuracy of 92.3% (+- 0.8%) with a per-frame inference time of 9.5 ms. Achieved sub-10 millisecond latency, combined with sparse spike activity (8.1%), enables real-time, low-power edge deployment. Unlike conventional CNN-based approaches, the hybrid SIFT-SNN pipeline explicitly preserves spatial feature grounding, enhances interpretability, supports transparent decision-making, and operates efficiently on embedded hardware. Although synthetic augmentation improved robustness, generalisation to unseen field conditions remains to be validated. The SIFT-SNN framework is validated through a working prototype deployed on a consumer-grade system and framed as a generalisable case study in structural safety monitoring for movable concrete barriers, which, as a traffic flow-control infrastructure, is deployed in over 20 cities worldwide.
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            <a href="https://www.alphaxiv.org/abs/2511.21256v1" target="_blank" rel="noopener noreferrer">
                LaGen：迈向自回归激光雷达场景生成
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            LaGen: Towards Autoregressive LiDAR Scene Generation
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Sizhuo Zhou, Xiaosong Jia, Fanrui Zhang, Junjie Li, Juyong Zhang, Yukang Feng, J...
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于激光雷达场景生成，属于纯粹的3D视觉领域，与推荐系统、搜索或广告的核心技术栈没有直接关联。激光雷达数据生成在自动驾驶和机器人技术中有应用，但无法直接应用于处理用户行为序列、内容特征或排名优化等推荐系统/搜索/广告的核心问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:39:16
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21256v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21256v1
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                    Generative world models for autonomous driving (AD) have become a trending topic. Unlike the widely studied image modality, in this work we explore generative world models for LiDAR data. Existing generation methods for LiDAR data only support single frame generation, while existing prediction approaches require multiple frames of historical input and can only deterministically predict multiple frames at once, lacking interactivity. Both paradigms fail to support long-horizon interactive generation. To this end, we introduce LaGen, which to the best of our knowledge is the first framework capable of frame-by-frame autoregressive generation of long-horizon LiDAR scenes. LaGen is able to take a single-frame LiDAR input as a starting point and effectively utilize bounding box information as conditions to generate high-fidelity 4D scene point clouds. In addition, we introduce a scene decoupling estimation module to enhance the model's interactive generation capability for object-level content, as well as a noise modulation module to mitigate error accumulation during long-horizon generation. We construct a protocol based on nuScenes for evaluating long-horizon LiDAR scene generation. Experimental results comprehensively demonstrate LaGen outperforms state-of-the-art LiDAR generation and prediction models, especially on the later frames.
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            <a href="https://www.alphaxiv.org/abs/2511.21251v1" target="_blank" rel="noopener noreferrer">
                AVFakeBench：面向音频-视频大语言模型的全方位音视频伪造检测基准
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            AVFakeBench: A Comprehensive Audio-Video Forgery Detection Benchmark for AV-LMMs
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuhan Xia, Peipei Li, Xuannan Liu, Dongsen Zhang, Xinyu Guo, Zekun Li
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音视频伪造检测基准测试，属于多媒体安全领域，与推荐系统、搜索或广告的核心技术无关。虽然涉及多模态模型，但其应用场景（伪造检测）与用户建模、内容排序或广告投放等核心业务没有直接关联，属于被排除的无关主题范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:33:12
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21251v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21251v1
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                    The threat of Audio-Video (AV) forgery is rapidly evolving beyond human-centric deepfakes to include more diverse manipulations across complex natural scenes. However, existing benchmarks are still confined to DeepFake-based forgeries and single-granularity annotations, thus failing to capture the diversity and complexity of real-world forgery scenarios. To address this, we introduce AVFakeBench, the first comprehensive audio-video forgery detection benchmark that spans rich forgery semantics across both human subject and general subject. AVFakeBench comprises 12K carefully curated audio-video questions, covering seven forgery types and four levels of annotations. To ensure high-quality and diverse forgeries, we propose a multi-stage hybrid forgery framework that integrates proprietary models for task planning with expert generative models for precise manipulation. The benchmark establishes a multi-task evaluation framework covering binary judgment, forgery types classification, forgery detail selection, and explanatory reasoning. We evaluate 11 Audio-Video Large Language Models (AV-LMMs) and 2 prevalent detection methods on AVFakeBench, demonstrating the potential of AV-LMMs as emerging forgery detectors while revealing their notable weaknesses in fine-grained perception and reasoning.
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            <a href="https://www.alphaxiv.org/abs/2511.21245v1" target="_blank" rel="noopener noreferrer">
                FIELDS：通过直接监督学习实现表情准确推理的人脸重建
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            FIELDS: Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Chen Ling, Henglin Shi, Hedvig Kjellström
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的人脸重建和表情推理，属于纯粹的视觉技术研究。虽然标题提到'学习'和'监督'，但内容明显属于人脸识别和3D重建范畴，与推荐系统、搜索或广告的核心技术没有任何直接关联，也不涉及Transformer架构或LLM技术的应用。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:22:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21245v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21245v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Facial expressions convey the bulk of emotional information in human communication, yet existing 3D face reconstruction methods often miss subtle affective details due to reliance on 2D supervision and lack of 3D ground truth. We propose FIELDS (Face reconstruction with accurate Inference of Expression using Learning with Direct Supervision) to address these limitations by extending self-supervised 2D image consistency cues with direct 3D expression parameter supervision and an auxiliary emotion recognition branch. Our encoder is guided by authentic expression parameters from spontaneous 4D facial scans, while an intensity-aware emotion loss encourages the 3D expression parameters to capture genuine emotion content without exaggeration. This dual-supervision strategy bridges the 2D/3D domain gap and mitigates expression-intensity bias, yielding high-fidelity 3D reconstructions that preserve subtle emotional cues. From a single image, FIELDS produces emotion-rich face models with highly realistic expressions, significantly improving in-the-wild facial expression recognition performance without sacrificing naturalness.
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            <a href="https://www.alphaxiv.org/abs/2511.21237v1" target="_blank" rel="noopener noreferrer">
                3-Tracer：一种用于音频伪造检测与定位的三级时序感知框架
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            3-Tracer: A Tri-level Temporal-Aware Framework for Audio Forgery Detection and Localization
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Shuhan Xia, Xuannan Liu, Xing Cui, Peipei Li
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于音频伪造检测和定位，属于多媒体安全领域，与推荐系统、搜索或广告的核心技术无关。虽然提到了时序感知框架，但这是针对音频信号处理的特定应用，没有显示出在RecSys/Search/Ads领域的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 10:07:03
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21237v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21237v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Recently, partial audio forgery has emerged as a new form of audio manipulation. Attackers selectively modify partial but semantically critical frames while preserving the overall perceptual authenticity, making such forgeries particularly difficult to detect. Existing methods focus on independently detecting whether a single frame is forged, lacking the hierarchical structure to capture both transient and sustained anomalies across different temporal levels. To address these limitations, We identify three key levels relevant to partial audio forgery detection and present T3-Tracer, the first framework that jointly analyzes audio at the frame, segment, and audio levels to comprehensively detect forgery traces. T3-Tracer consists of two complementary core modules: the Frame-Audio Feature Aggregation Module (FA-FAM) and the Segment-level Multi-Scale Discrepancy-Aware Module (SMDAM). FA-FAM is designed to detect the authenticity of each audio frame. It combines both frame-level and audio-level temporal information to detect intra-frame forgery cues and global semantic inconsistencies. To further refine and correct frame detection, we introduce SMDAM to detect forgery boundaries at the segment level. It adopts a dual-branch architecture that jointly models frame features and inter-frame differences across multi-scale temporal windows, effectively identifying abrupt anomalies that appeared on the forged boundaries. Extensive experiments conducted on three challenging datasets demonstrate that our approach achieves state-of-the-art performance.
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            <a href="https://www.alphaxiv.org/abs/2511.21194v1" target="_blank" rel="noopener noreferrer">
                BotaCLIP：面向地球观测数据的植物学感知表征对比学习
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            BotaCLIP: Contrastive Learning for Botany-Aware Representation of Earth Observation Data
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Selene Cerna, Sara Si-Moussi, Wilfried Thuiller, Hadrien Hendrikx, Vincent Miele
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于植物学领域的地球观测数据，属于特定领域应用（生物学/地理信息），与推荐系统、搜索或广告的核心技术进展无关。论文中的对比学习技术虽然通用，但缺乏与RecSys/Search/Ads领域的明确关联或潜在应用路径。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:19:06
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                <a href="https://arxiv.org/abs/2511.21194v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21194v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Foundation models have demonstrated a remarkable ability to learn rich, transferable representations across diverse modalities such as images, text, and audio. In modern machine learning pipelines, these representations often replace raw data as the primary input for downstream tasks. In this paper, we address the challenge of adapting a pre-trained foundation model to inject domain-specific knowledge, without retraining from scratch or incurring significant computational costs. To this end, we introduce BotaCLIP, a lightweight multimodal contrastive framework that adapts a pre-trained Earth Observation foundation model (DOFA) by aligning high-resolution aerial imagery with botanical relevés. Unlike generic embeddings, BotaCLIP internalizes ecological structure through contrastive learning with a regularization strategy that mitigates catastrophic forgetting. Once trained, the resulting embeddings serve as transferable representations for downstream predictors. Motivated by real-world applications in biodiversity modeling, we evaluated BotaCLIP representations in three ecological tasks: plant presence prediction, butterfly occurrence modeling, and soil trophic group abundance estimation. The results showed consistent improvements over those derived from DOFA and supervised baselines. More broadly, this work illustrates how domain-aware adaptation of foundation models can inject expert knowledge into data-scarce settings, enabling frugal representation learning.
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                当机器人服从补丁：针对视觉-语言-动作模型的通用可迁移补丁攻击
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            When Robots Obey the Patch: Universal Transferable Patch Attacks on Vision-Language-Action Models
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hui Lu, Yi Yu, Yiming Yang, Chenyu Yi, Qixin Zhang, Bingquan Shen, Alex C. Kot, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文聚焦于视觉-语言-动作模型的安全漏洞和对抗性攻击，属于安全领域而非推荐系统、搜索或广告的核心技术。虽然涉及多模态模型，但其研究方向（攻击与防御）与当前关注的领域进展、使能技术或直接应用无关，且安全主题被明确列为不相关内容。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 09:16:32
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21192v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21192v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Vision-Language-Action (VLA) models are vulnerable to adversarial attacks, yet universal and transferable attacks remain underexplored, as most existing patches overfit to a single model and fail in black-box settings. To address this gap, we present a systematic study of universal, transferable adversarial patches against VLA-driven robots under unknown architectures, finetuned variants, and sim-to-real shifts. We introduce UPA-RFAS (Universal Patch Attack via Robust Feature, Attention, and Semantics), a unified framework that learns a single physical patch in a shared feature space while promoting cross-model transfer. UPA-RFAS combines (i) a feature-space objective with an $\ell_1$ deviation prior and repulsive InfoNCE loss to induce transferable representation shifts, (ii) a robustness-augmented two-phase min-max procedure where an inner loop learns invisible sample-wise perturbations and an outer loop optimizes the universal patch against this hardened neighborhood, and (iii) two VLA-specific losses: Patch Attention Dominance to hijack text$\to$vision attention and Patch Semantic Misalignment to induce image-text mismatch without labels. Experiments across diverse VLA models, manipulation suites, and physical executions show that UPA-RFAS consistently transfers across models, tasks, and viewpoints, exposing a practical patch-based attack surface and establishing a strong baseline for future defenses.
                </div>
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        <h3 class="text-base font-medium text-primary hover:underline transition-colors">
            <a href="https://www.alphaxiv.org/abs/2511.21145v1" target="_blank" rel="noopener noreferrer">
                TEAR：面向文本到视频模型的时序感知自动化红队测试
            </a>
        </h3>
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            <i class="fa fa-star mr-1"></i>1/10
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        <div class="mb-2 text-base text-gray-700">
            TEAR: Temporal-aware Automated Red-teaming for Text-to-Video Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Jiaming He, Guanyu Hou, Hongwei Li, Zhicong Huang, Kangjie Chen, Yi Yu, Wenbo Ji...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于文本到视频模型的安全测试和红队测试，属于内容生成领域的特定安全评估范畴。这与我的核心关注点（推荐系统、搜索、广告中的核心进展、LLM使能技术及其直接应用）完全无关。红队测试和安全评估被明确列为不相关主题。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:58:42
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21145v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21145v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Text-to-Video (T2V) models are capable of synthesizing high-quality, temporally coherent dynamic video content, but the diverse generation also inherently introduces critical safety challenges. Existing safety evaluation methods,which focus on static image and text generation, are insufficient to capture the complex temporal dynamics in video generation. To address this, we propose a TEmporal-aware Automated Red-teaming framework, named TEAR, an automated framework designed to uncover safety risks specifically linked to the dynamic temporal sequencing of T2V models. TEAR employs a temporal-aware test generator optimized via a two-stage approach: initial generator training and temporal-aware online preference learning, to craft textually innocuous prompts that exploit temporal dynamics to elicit policy-violating video output. And a refine model is adopted to improve the prompt stealthiness and adversarial effectiveness cyclically. Extensive experimental evaluation demonstrates the effectiveness of TEAR across open-source and commercial T2V systems with over 80% attack success rate, a significant boost from prior best result of 57%.
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            <a href="https://www.alphaxiv.org/abs/2511.21143v1" target="_blank" rel="noopener noreferrer">
                STAR：增强现实中的智能手机类比打字
            </a>
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            STAR: Smartphone-analogous Typing in Augmented Reality
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taejun Kim, Amy Karlson, Aakar Gupta, Tovi Grossman, Jason Wu, Parastoo Abtahi, ...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及增强现实(AR)中的输入交互技术，属于人机交互领域。虽然AR技术可能有潜在的广告展示应用，但标题明确聚焦于打字输入这一特定交互场景，与推荐系统、搜索或广告的核心排名和建模技术没有直接关联。该研究主要解决AR环境中的文本输入问题，属于纯粹的交互技术范畴。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:53:09
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21143v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21143v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.HC</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    While text entry is an essential and frequent task in Augmented Reality (AR) applications, devising an efficient and easy-to-use text entry method for AR remains an open challenge. This research presents STAR, a smartphone-analogous AR text entry technique that leverages a user's familiarity with smartphone two-thumb typing. With STAR, a user performs thumb typing on a virtual QWERTY keyboard that is overlain on the skin of their hands. During an evaluation study of STAR, participants achieved a mean typing speed of 21.9 WPM (i.e., 56% of their smartphone typing speed), and a mean error rate of 0.3% after 30 minutes of practice. We further analyze the major factors implicated in the performance gap between STAR and smartphone typing, and discuss ways this gap could be narrowed.
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            <a href="https://www.alphaxiv.org/abs/2511.21136v1" target="_blank" rel="noopener noreferrer">
                基于熵引导优先级渐进学习的高效人体视频生成训练
            </a>
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        <div class="mb-2 text-base text-gray-700">
            Efficient Training for Human Video Generation with Entropy-Guided Prioritized Progressive Learning
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Changlin Li, Jiawei Zhang, Shuhao Liu, Sihao Lin, Zeyi Shi, Zhihui Li, Xiaojun C...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于人体视频生成，属于纯粹的视觉生成任务，与推荐系统、搜索或广告的核心技术无关。虽然提到了高效训练方法，但没有明确的应用场景或技术迁移潜力指向RecSys/Search/Ads领域。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:36:37
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21136v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21136v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Human video generation has advanced rapidly with the development of diffusion models, but the high computational cost and substantial memory consumption associated with training these models on high-resolution, multi-frame data pose significant challenges. In this paper, we propose Entropy-Guided Prioritized Progressive Learning (Ent-Prog), an efficient training framework tailored for diffusion models on human video generation. First, we introduce Conditional Entropy Inflation (CEI) to assess the importance of different model components on the target conditional generation task, enabling prioritized training of the most critical components. Second, we introduce an adaptive progressive schedule that adaptively increases computational complexity during training by measuring the convergence efficiency. Ent-Prog reduces both training time and GPU memory consumption while maintaining model performance. Extensive experiments across three datasets, demonstrate the effectiveness of Ent-Prog, achieving up to 2.2$\times$ training speedup and 2.4$\times$ GPU memory reduction without compromising generative performance.
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            <a href="https://www.alphaxiv.org/abs/2511.21135v1" target="_blank" rel="noopener noreferrer">
                SocialNav：训练受人类启发的社交感知具身导航基础模型
            </a>
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        <div class="mb-2 text-base text-gray-700">
            SocialNav: Training Human-Inspired Foundation Model for Socially-Aware Embodied Navigation
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Ziyi Chen, Yingnan Guo, Zedong Chu, Minghua Luo, Yanfen Shen, Mingchao Sun, Junj...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于具身导航和社交感知，属于机器人学和计算机视觉领域，与推荐系统、搜索或广告的核心技术没有直接关联。虽然标题提到'基础模型'，但其应用场景（社交导航）与我的关注领域（RecSys/Search/Ads）完全不相关，无法识别出任何潜在的应用价值。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:36:01
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21135v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21135v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.RO</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Embodied navigation that adheres to social norms remains an open research challenge. Our \textbf{SocialNav} is a foundational model for socially-aware navigation with a hierarchical "brain-action" architecture, capable of understanding high-level social norms and generating low-level, socially compliant trajectories. To enable such dual capabilities, we construct the SocNav Dataset, a large-scale collection of 7 million samples, comprising (1) a Cognitive Activation Dataset providing social reasoning signals such as chain-of-thought explanations and social traversability prediction, and (2) an Expert Trajectories Pyramid aggregating diverse navigation demonstrations from internet videos, simulated environments, and real-world robots. A multi-stage training pipeline is proposed to gradually inject and refine navigation intelligence: we first inject general navigation skills and social norms understanding into the model via imitation learning, and then refine such skills through a deliberately designed Socially-Aware Flow Exploration GRPO (SAFE-GRPO), the first flow-based reinforcement learning framework for embodied navigation that explicitly rewards socially compliant behaviors. SocialNav achieves +38% success rate and +46% social compliance rate compared to the state-of-the-art method, demonstrating strong gains in both navigation performance and social compliance. Our project page: https://amap-eai.github.io/SocialNav/
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            <a href="https://www.alphaxiv.org/abs/2511.21132v1" target="_blank" rel="noopener noreferrer">
                DeepRFTv2：用于图像去模糊的核级学习
            </a>
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        <div class="mb-2 text-base text-gray-700">
            DeepRFTv2: Kernel-level Learning for Image Deblurring
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xintian Mao, Haofei Song, Yin-Nian Liu, Qingli Li, Yan Wang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的图像去模糊技术，属于纯粹的图像处理范畴。虽然标题提到'核级学习'，但这是针对图像模糊核的特定技术，没有显示出与推荐系统、搜索或广告领域的直接关联或潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:30:41
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21132v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21132v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    It is well-known that if a network aims to learn how to deblur, it should understand the blur process. Blurring is naturally caused by the convolution of the sharp image with the blur kernel. Thus, allowing the network to learn the blur process in the kernel-level can significantly improve the image deblurring performance. But, current deep networks are still at the pixel-level learning stage, either performing end-to-end pixel-level restoration or stage-wise pseudo kernel-level restoration, failing to enable the deblur model to understand the essence of the blur. To this end, we propose Fourier Kernel Estimator (FKE), which considers the activation operation in Fourier space and converts the convolution problem in the spatial domain to a multiplication problem in Fourier space. Our FKE, jointly optimized with the deblur model, enables the network to learn the kernel-level blur process with low complexity and without any additional supervision. Furthermore, we change the convolution object of the kernel from ``image" to network extracted ``feature", whose rich semantic and structural information is more suitable to blur process learning. With the convolution of the feature and the estimated kernel, our model can learn the essence of blur in kernel-level. To further improve the efficiency of feature extraction, we design a decoupled multi-scale architecture with multiple hierarchical sub-unets with a reversible strategy, which allows better multi-scale encoding and decoding in low training memory. Extensive experiments indicate that our method achieves state-of-the-art motion deblurring results and show potential for handling other kernel-related problems. Analysis also shows our kernel estimator is able to learn physically meaningful kernels. The code will be available at https://github.com/DeepMed-Lab-ECNU/Single-Image-Deblur.
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            <a href="https://www.alphaxiv.org/abs/2511.21122v1" target="_blank" rel="noopener noreferrer">
                哪一层导致分布偏差？基于熵指导的自适应剪枝用于扩散和流模型
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            Which Layer Causes Distribution Deviation? Entropy-Guided Adaptive Pruning for Diffusion and Flow Models
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Changlin Li, Jiawei Zhang, Zeyi Shi, Zongxin Yang, Zhihui Li, Xiaojun Chang
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于扩散模型和流模型的剪枝技术，属于生成模型的优化范畴。虽然扩散模型在AIGC和内容生成中有应用，但根据用户明确的排除标准（'AIGC, Content generation, Summarization, or other purely LLM-centric topics'），这属于不相关主题。论文没有展示与推荐系统、搜索或广告的直接或潜在关联。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 07:20:48
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21122v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21122v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Large-scale vision generative models, including diffusion and flow models, have demonstrated remarkable performance in visual generation tasks. However, transferring these pre-trained models to downstream tasks often results in significant parameter redundancy. In this paper, we propose EntPruner, an entropy-guided automatic progressive pruning framework for diffusion and flow models. First, we introduce entropy-guided pruning, a block-level importance assessment strategy specifically designed for generative models. Unlike discriminative models, generative models require preserving the diversity and condition-fidelity of the output distribution. As the importance of each module can vary significantly across downstream tasks, EntPruner prioritizes pruning of less important blocks using data-dependent Conditional Entropy Deviation (CED) as a guiding metric. CED quantifies how much the distribution diverges from the learned conditional data distribution after removing a block. Second, we propose a zero-shot adaptive pruning framework to automatically determine when and how much to prune during training. This dynamic strategy avoids the pitfalls of one-shot pruning, mitigating mode collapse, and preserving model performance. Extensive experiments on DiT and SiT models demonstrate the effectiveness of EntPruner, achieving up to 2.22$\times$ inference speedup while maintaining competitive generation quality on ImageNet and three downstream datasets.
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            <a href="https://www.alphaxiv.org/abs/2511.21114v1" target="_blank" rel="noopener noreferrer">
                用于阿尔茨海默病早期预测的形变感知时序生成
            </a>
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            Deformation-aware Temporal Generation for Early Prediction of Alzheimers Disease
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Honga, Jie Lin, Minghui Wang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于医学领域的阿尔茨海默病预测，属于明确的无关主题。尽管提到了时序生成技术，但其应用场景完全限定在医疗诊断领域，与推荐系统、搜索或广告没有任何关联。论文内容不涉及任何可应用于RecSys/Search/Ads的技术或方法。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:59:17
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21114v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21114v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span></div>
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                    Alzheimer's disease (AD), a degenerative brain condition, can benefit from early prediction to slow its progression. As the disease progresses, patients typically undergo brain atrophy. Current prediction methods for Alzheimers disease largely involve analyzing morphological changes in brain images through manual feature extraction. This paper proposes a novel method, the Deformation-Aware Temporal Generative Network (DATGN), to automate the learning of morphological changes in brain images about disease progression for early prediction. Given the common occurrence of missing data in the temporal sequences of MRI images, DATGN initially interpolates incomplete sequences. Subsequently, a bidirectional temporal deformation-aware module guides the network in generating future MRI images that adhere to the disease's progression, facilitating early prediction of Alzheimer's disease. DATGN was tested for the generation of temporal sequences of future MRI images using the ADNI dataset, and the experimental results are competitive in terms of PSNR and MMSE image quality metrics. Furthermore, when DATGN-generated synthetic data was integrated into the SVM vs. CNN vs. 3DCNN-based classification methods, significant improvements were achieved from 6. 21\% to 16\% in AD vs. NC classification accuracy and from 7. 34\% to 21. 25\% in AD vs. MCI vs. NC classification accuracy. The qualitative visualization results indicate that DATGN produces MRI images consistent with the brain atrophy trend in Alzheimer's disease, enabling early disease prediction.
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            <a href="https://www.alphaxiv.org/abs/2511.21113v1" target="_blank" rel="noopener noreferrer">
                FaithFusion：通过逐像素信息增益协调重建与生成
            </a>
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            FaithFusion: Harmonizing Reconstruction and Generation via Pixel-wise Information Gain
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>YuAn Wang, Xiaofan Li, Chi Huang, Wenhao Zhang, Hao Li, Bosheng Wang, Xun Sun, J...
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题聚焦于图像生成与重建的协调方法，涉及像素级信息增益，这属于纯粹的计算机视觉和图像生成领域。没有证据表明该方法与推荐系统、搜索或广告有直接关联，也不涉及Transformer架构改进或LLM技术在相关领域的应用潜力。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:58:57
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21113v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21113v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    In controllable driving-scene reconstruction and 3D scene generation, maintaining geometric fidelity while synthesizing visually plausible appearance under large viewpoint shifts is crucial. However, effective fusion of geometry-based 3DGS and appearance-driven diffusion models faces inherent challenges, as the absence of pixel-wise, 3D-consistent editing criteria often leads to over-restoration and geometric drift. To address these issues, we introduce \textbf{FaithFusion}, a 3DGS-diffusion fusion framework driven by pixel-wise Expected Information Gain (EIG). EIG acts as a unified policy for coherent spatio-temporal synthesis: it guides diffusion as a spatial prior to refine high-uncertainty regions, while its pixel-level weighting distills the edits back into 3DGS. The resulting plug-and-play system is free from extra prior conditions and structural modifications.Extensive experiments on the Waymo dataset demonstrate that our approach attains SOTA performance across NTA-IoU, NTL-IoU, and FID, maintaining an FID of 107.47 even at 6 meters lane shift. Our code is available at https://github.com/wangyuanbiubiubiu/FaithFusion.
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            <a href="https://www.alphaxiv.org/abs/2511.21105v1" target="_blank" rel="noopener noreferrer">
                面向雷达场景理解的规模化基础模型
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            Scaling Foundation Models for Radar Scene Understanding
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Pushkal Mishra, Kshitiz Bansal, Dinesh Bharadia
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于雷达场景理解这一特定传感器模态，与推荐系统、搜索或广告领域没有直接关联。雷达数据属于物理传感器领域，无法直接应用于处理用户行为、内容特征或上下文信息等推荐/搜索核心数据。该技术缺乏在异构数据统一建模或Transformer架构效率方面的通用性应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:41:00
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21105v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21105v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Radar sensors provide reliable perception across adverse weather, lighting, and long-range conditions. Recent advances in foundation models have transformed visual and language understanding, yet their integration with radar sensing remains largely underexplored. Existing radar approaches are fragmented and task-specific; each downstream task employs distinct architectures and training objectives, preventing transfer across tasks. In this work, we introduce RadarFM: a radar foundation model that learns unified scene-level representations through structured spatial language supervision. We make two key contributions: (1) a structured caption framework that encodes vehicle distributions in native radar coordinates, and (2) a hash-aware contrastive learning objective that quantifies continuous scene similarity rather than binary matching, enabling fine-grained spatial reasoning. Leveraging the CARLA simulator, we generate large-scale, well-annotated radar datasets across diverse driving scenarios. We also propose localization-aware metrics that assess spatial accuracy beyond traditional detection measures.
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            <a href="https://www.alphaxiv.org/abs/2511.21098v1" target="_blank" rel="noopener noreferrer">
                视觉中的皮格马利翁效应：用于反射几何重建的图像到黏土转换
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            Pygmalion Effect in Vision: Image-to-Clay Translation for Reflective Geometry Reconstruction
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Gayoung Lee, Junho Kim, Jin-Hwa Kim, Junmo Kim
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像到3D几何重建任务，特别是黏土模型的生成。这属于纯粹的视觉领域研究，没有展示与推荐系统、搜索或广告的明显关联。标题中提到的反射几何重建和图像转换技术缺乏在RecSys/Search/Ads领域的潜在应用场景。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:34:58
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21098v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21098v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.GR</span></div>
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                    Understanding reflection remains a long-standing challenge in 3D reconstruction due to the entanglement of appearance and geometry under view-dependent reflections. In this work, we present the Pygmalion Effect in Vision, a novel framework that metaphorically "sculpts" reflective objects into clay-like forms through image-to-clay translation. Inspired by the myth of Pygmalion, our method learns to suppress specular cues while preserving intrinsic geometric consistency, enabling robust reconstruction from multi-view images containing complex reflections. Specifically, we introduce a dual-branch network in which a BRDF-based reflective branch is complemented by a clay-guided branch that stabilizes geometry and refines surface normals. The two branches are trained jointly using the synthesized clay-like images, which provide a neutral, reflection-free supervision signal that complements the reflective views. Experiments on both synthetic and real datasets demonstrate substantial improvement in normal accuracy and mesh completeness over existing reflection-handling methods. Beyond technical gains, our framework reveals that seeing by unshining, translating radiance into neutrality, can serve as a powerful inductive bias for reflective object geometry learning.
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            <a href="https://www.alphaxiv.org/abs/2511.21097v1" target="_blank" rel="noopener noreferrer">
                CLRecogEye：利用课程学习开发卷积特征进行动态虹膜识别
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            CLRecogEye : Curriculum Learning towards exploiting convolution features for Dynamic Iris Recognition
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, Raghavendra Ramachandra
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于生物识别领域的虹膜识别技术，涉及计算机视觉中的卷积特征和课程学习方法。这与推荐系统、搜索或广告的核心领域完全无关，也不涉及任何LLM、Transformer技术或异构数据处理方法。论文内容属于纯粹的计算机视觉应用，没有明确的RecSys/Search/Ads应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 06:32:22
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21097v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21097v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Iris authentication algorithms have achieved impressive recognition performance, making them highly promising for real-world applications such as border control, citizen identification, and both criminal investigations and commercial systems. However, their robustness is still challenged by variations in rotation, scale, specular reflections, and defocus blur. In addition, most existing approaches rely on straightforward point-to-point comparisons, typically using cosine or L2 distance, without effectively leveraging the spatio-spatial-temporal structure of iris patterns. To address these limitations, we propose a novel and generalized matching pipeline that learns rich spatio-spatial-temporal representations of iris features. Our approach first splits each iris image along one dimension, generating a sequence of sub-images that serve as input to a 3D-CNN, enabling the network to capture both spatial and spatio-spatial-temporal cues. To further enhance the modeling of spatio-spatial-temporal feature dynamics, we train the model in curriculum manner. This design allows the network to embed temporal dependencies directly into the feature space, improving discriminability in the deep metric domain. The framework is trained end-to-end with triplet and ArcFace loss in a curriculum manner, enforcing highly discriminative embeddings despite challenges like rotation, scale, reflections, and blur. This design yields a robust and generalizable solution for iris authentication.Github code: https://github.com/GeetanjaliGTZ/CLRecogEye
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                阿尔茨海默病长期预测：基于非均匀时间序列上采用正态逆伽马分布进行时序参数估计的新型图像生成方法
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        <div class="mb-2 text-base text-gray-700">
            Long-Term Alzheimers Disease Prediction: A Novel Image Generation Method Using Temporal Parameter Estimation with Normal Inverse Gamma Distribution on Uneven Time Series
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Xin Hong, Xinze Sun, Yinhao Li, Yen-Wei Chen
        </div>
        
        
        
        
        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于医学领域的阿尔茨海默病预测，涉及医学图像生成和疾病诊断，属于明确的医疗应用范畴。论文内容与推荐系统、搜索或广告领域没有任何关联，也不涉及LLM技术或Transformer架构的进展，完全属于用户指定的不相关主题范围。</p>
        </div>
        
        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:49:43
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21057v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21057v1
                </a>
                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Image generation can provide physicians with an imaging diagnosis basis in the prediction of Alzheimer's Disease (AD). Recent research has shown that long-term AD predictions by image generation often face difficulties maintaining disease-related characteristics when dealing with irregular time intervals in sequential data. Considering that the time-related aspects of the distribution can reflect changes in disease-related characteristics when images are distributed unevenly, this research proposes a model to estimate the temporal parameter within the Normal Inverse Gamma Distribution (T-NIG) to assist in generating images over the long term. The T-NIG model employs brain images from two different time points to create intermediate brain images, forecast future images, and predict the disease. T-NIG is designed by identifying features using coordinate neighborhoods. It incorporates a time parameter into the normal inverse gamma distribution to understand how features change in brain imaging sequences that have varying time intervals. Additionally, T-NIG utilizes uncertainty estimation to reduce both epistemic and aleatoric uncertainties in the model, which arise from insufficient temporal data. In particular, the T-NIG model demonstrates state-of-the-art performance in both short-term and long-term prediction tasks within the dataset. Experimental results indicate that T-NIG is proficient in forecasting disease progression while maintaining disease-related characteristics, even when faced with an irregular temporal data distribution.
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            <a href="https://www.alphaxiv.org/abs/2511.21051v1" target="_blank" rel="noopener noreferrer">
                MUSE：通过测试时优化在图像中合成情感的操纵统一框架
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            MUSE: Manipulating Unified Framework for Synthesizing Emotions in Images via Test-Time Optimization
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingjie Xia, Xi Wang, Jinglei Shi, Vicky Kalogeiton, Jian Yang
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于图像中的情感合成和操纵，属于纯粹的视觉生成任务。虽然标题提到'统一框架'，但核心内容与推荐系统、搜索或广告中的排名、检索或用户建模无关。该技术主要面向图像生成和编辑，没有明显的应用场景可以转化为RecSys/Search/Ads领域的核心问题。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:39:25
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21051v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21051v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Images evoke emotions that profoundly influence perception, often prioritized over content. Current Image Emotional Synthesis (IES) approaches artificially separate generation and editing tasks, creating inefficiencies and limiting applications where these tasks naturally intertwine, such as therapeutic interventions or storytelling. In this work, we introduce MUSE, the first unified framework capable of both emotional generation and editing. By adopting a strategy conceptually aligned with Test-Time Scaling (TTS) that widely used in both LLM and diffusion model communities, it avoids the requirement for additional updating diffusion model and specialized emotional synthesis datasets. More specifically, MUSE addresses three key questions in emotional synthesis: (1) HOW to stably guide synthesis by leveraging an off-the-shelf emotion classifier with gradient-based optimization of emotional tokens; (2) WHEN to introduce emotional guidance by identifying the optimal timing using semantic similarity as a supervisory signal; and (3) WHICH emotion to guide synthesis through a multi-emotion loss that reduces interference from inherent and similar emotions. Experimental results show that MUSE performs favorably against all methods for both generation and editing, improving emotional accuracy and semantic diversity while maintaining an optimal balance between desired content, adherence to text prompts, and realistic emotional expression. It establishes a new paradigm for emotion synthesis.
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            <a href="https://www.alphaxiv.org/abs/2511.21043v1" target="_blank" rel="noopener noreferrer">
                PG-ControlNet：一种用于生成式空间变化图像去模糊的物理引导控制网络
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            PG-ControlNet: A Physics-Guided ControlNet for Generative Spatially Varying Image Deblurring
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Hakki Motorcu, Mujdat Cetin
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉中的图像去模糊任务，属于纯粹的视觉处理领域。虽然涉及生成模型，但主要应用于图像质量增强，与推荐系统、搜索或广告的排名、检索、用户建模等核心任务没有直接关联。论文的物理引导方法和空间变化处理技术在当前焦点领域缺乏明确的应用潜力。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:19:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21043v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21043v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Spatially varying image deblurring remains a fundamentally ill-posed problem, especially when degradations arise from complex mixtures of motion and other forms of blur under significant noise. State-of-the-art learning-based approaches generally fall into two paradigms: model-based deep unrolling methods that enforce physical constraints by modeling the degradations, but often produce over-smoothed, artifact-laden textures, and generative models that achieve superior perceptual quality yet hallucinate details due to weak physical constraints. In this paper, we propose a novel framework that uniquely reconciles these paradigms by taming a powerful generative prior with explicit, dense physical constraints. Rather than oversimplifying the degradation field, we model it as a dense continuum of high-dimensional compressed kernels, ensuring that minute variations in motion and other degradation patterns are captured. We leverage this rich descriptor field to condition a ControlNet architecture, strongly guiding the diffusion sampling process. Extensive experiments demonstrate that our method effectively bridges the gap between physical accuracy and perceptual realism, outperforming state-of-the-art model-based methods as well as generative baselines in challenging, severely blurred scenarios.
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            <a href="https://www.alphaxiv.org/abs/2511.21042v1" target="_blank" rel="noopener noreferrer">
                LungNoduleAgent：一种用于肺结节精准诊断的协作多智能体系统
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            LungNoduleAgent: A Collaborative Multi-Agent System for Precision Diagnosis of Lung Nodules
        </div>
        
        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Cheng Yang, Hui Jin, Xinlei Yu, Zhipeng Wang, Yaoqun Liu, Fenglei Fan, Dajiang L...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题明确聚焦于医学领域的肺结节诊断应用，属于明确的医疗领域特定应用。多智能体系统虽然是一种技术方法，但论文的应用场景（肺结节诊断）完全落在医疗生物学领域，与搜索、推荐、广告等核心关注领域无任何关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:19:27
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21042v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21042v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Diagnosing lung cancer typically involves physicians identifying lung nodules in Computed tomography (CT) scans and generating diagnostic reports based on their morphological features and medical expertise. Although advancements have been made in using multimodal large language models for analyzing lung CT scans, challenges remain in accurately describing nodule morphology and incorporating medical expertise. These limitations affect the reliability and effectiveness of these models in clinical settings. Collaborative multi-agent systems offer a promising strategy for achieving a balance between generality and precision in medical applications, yet their potential in pathology has not been thoroughly explored. To bridge these gaps, we introduce LungNoduleAgent, an innovative collaborative multi-agent system specifically designed for analyzing lung CT scans. LungNoduleAgent streamlines the diagnostic process into sequential components, improving precision in describing nodules and grading malignancy through three primary modules. The first module, the Nodule Spotter, coordinates clinical detection models to accurately identify nodules. The second module, the Radiologist, integrates localized image description techniques to produce comprehensive CT reports. Finally, the Doctor Agent System performs malignancy reasoning by using images and CT reports, supported by a pathology knowledge base and a multi-agent system framework. Extensive testing on two private datasets and the public LIDC-IDRI dataset indicates that LungNoduleAgent surpasses mainstream vision-language models, agent systems, and advanced expert models. These results highlight the importance of region-level semantic alignment and multi-agent collaboration in diagnosing nodules. LungNoduleAgent stands out as a promising foundational tool for supporting clinical analyses of lung nodules.
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            <a href="https://www.alphaxiv.org/abs/2511.21040v1" target="_blank" rel="noopener noreferrer">
                基于软件定义无线电的空中自动调制分类的CNN-LSTM混合架构
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            CNN-LSTM Hybrid Architecture for Over-the-Air Automatic Modulation Classification Using SDR
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Dinanath Padhya, Krishna Acharya, Bipul Kumar Dahal, Dinesh Baniya Kshatri
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于信号处理和通信领域的调制分类技术，使用CNN-LSTM混合架构处理无线信号数据。这与推荐系统、搜索或广告的核心技术栈完全无关，也不涉及Transformer架构、LLM技术或异构数据统一建模等关注领域。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 04:16:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21040v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21040v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.CV</span></div>
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                    Automatic Modulation Classification (AMC) is a core technology for future wireless communication systems, enabling the identification of modulation schemes without prior knowledge. This capability is essential for applications in cognitive radio, spectrum monitoring, and intelligent communication networks. We propose an AMC system based on a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture, integrated with a Software Defined Radio (SDR) platform. The proposed architecture leverages CNNs for spatial feature extraction and LSTMs for capturing temporal dependencies, enabling efficient handling of complex, time-varying communication signals. The system's practical ability was demonstrated by identifying over-the-air (OTA) signals from a custom-built FM transmitter alongside other modulation schemes. The system was trained on a hybrid dataset combining the RadioML2018 dataset with a custom-generated dataset, featuring samples at Signal-to-Noise Ratios (SNRs) from 0 to 30dB. System performance was evaluated using accuracy, precision, recall, F1 score, and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The optimized model achieved 93.48% accuracy, 93.53% precision, 93.48% recall, and an F1 score of 93.45%. The AUC-ROC analysis confirmed the model's discriminative power, even in noisy conditions. This paper's experimental results validate the effectiveness of the hybrid CNN-LSTM architecture for AMC, suggesting its potential application in adaptive spectrum management and advanced cognitive radio systems.
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            <a href="https://www.alphaxiv.org/abs/2511.21029v1" target="_blank" rel="noopener noreferrer">
                FlowerDance：用于高效精细3D舞蹈生成的MeanFlow方法
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            FlowerDance: MeanFlow for Efficient and Refined 3D Dance Generation
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Kaixing Yang, Xulong Tang, Ziqiao Peng, Xiangyue Zhang, Puwei Wang, Jun He, Hong...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于3D舞蹈生成，属于纯粹的计算机图形学和动作生成领域。虽然标题中提到'高效'，但核心内容是3D舞蹈生成，与推荐系统、搜索或广告没有任何直接或间接的关联。这完全属于被排除的'纯粹视觉、3D视觉或图形学'范畴。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:53:10
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21029v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21029v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Music-to-dance generation aims to translate auditory signals into expressive human motion, with broad applications in virtual reality, choreography, and digital entertainment. Despite promising progress, the limited generation efficiency of existing methods leaves insufficient computational headroom for high-fidelity 3D rendering, thereby constraining the expressiveness of 3D characters during real-world applications. Thus, we propose FlowerDance, which not only generates refined motion with physical plausibility and artistic expressiveness, but also achieves significant generation efficiency on inference speed and memory utilization . Specifically, FlowerDance combines MeanFlow with Physical Consistency Constraints, which enables high-quality motion generation with only a few sampling steps. Moreover, FlowerDance leverages a simple but efficient model architecture with BiMamba-based backbone and Channel-Level Cross-Modal Fusion, which generates dance with efficient non-autoregressive manner. Meanwhile, FlowerDance supports motion editing, enabling users to interactively refine dance sequences. Extensive experiments on AIST++ and FineDance show that FlowerDance achieves state-of-the-art results in both motion quality and generation efficiency. Code will be released upon acceptance.
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            <a href="https://www.alphaxiv.org/abs/2511.21024v1" target="_blank" rel="noopener noreferrer">
                CameraMaster：用于摄影后期处理的统一相机语义-参数控制
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            CameraMaster: Unified Camera Semantic-Parameter Control for Photography Retouching
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Qirui Yang, Yang Yang, Ying Zeng, Xiaobin Hu, Bo Li, Huanjing Yue, Jingyu Yang, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于摄影后期处理和相机参数控制，属于计算机视觉和图像处理领域。虽然标题提到'语义控制'，但这主要针对摄影技术参数而非推荐系统、搜索或广告应用。该研究没有显示出与大规模推荐系统、搜索排名或广告投放的直接关联，也不涉及LLM技术或Transformer架构的进展。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:42:34
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21024v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21024v1
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                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Text-guided diffusion models have greatly advanced image editing and generation. However, achieving physically consistent image retouching with precise parameter control (e.g., exposure, white balance, zoom) remains challenging. Existing methods either rely solely on ambiguous and entangled text prompts, which hinders precise camera control, or train separate heads/weights for parameter adjustment, which compromises scalability, multi-parameter composition, and sensitivity to subtle variations. To address these limitations, we propose CameraMaster, a unified camera-aware framework for image retouching. The key idea is to explicitly decouple the camera directive and then coherently integrate two critical information streams: a directive representation that captures the photographer's intent, and a parameter embedding that encodes precise camera settings. CameraMaster first uses the camera parameter embedding to modulate both the camera directive and the content semantics. The modulated directive is then injected into the content features via cross-attention, yielding a strongly camera-sensitive semantic context. In addition, the directive and camera embeddings are injected as conditioning and gating signals into the time embedding, enabling unified, layer-wise modulation throughout the denoising process and enforcing tight semantic-parameter alignment. To train and evaluate CameraMaster, we construct a large-scale dataset of 78K image-prompt pairs annotated with camera parameters. Extensive experiments show that CameraMaster produces monotonic and near-linear responses to parameter variations, supports seamless multi-parameter composition, and significantly outperforms existing methods.
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            <a href="https://www.alphaxiv.org/abs/2511.21019v1" target="_blank" rel="noopener noreferrer">
                基于自回归条件生成对抗网络的概率性野火蔓延预测
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            Probabilistic Wildfire Spread Prediction Using an Autoregressive Conditional Generative Adversarial Network
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Taehoon Kang, Taeyong Kim
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于野火蔓延预测这一特定领域应用，与推荐系统、搜索或广告的核心技术完全无关。虽然使用了生成对抗网络技术，但该技术在此处的应用仅限于环境科学领域，没有显示出在推荐系统、搜索或广告中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 03:32:54
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.21019v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.21019v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.LG</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CE</span><span class="category-tag">cs.CV</span></div>
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                    Climate change has intensified the frequency and severity of wildfires, making rapid and accurate prediction of fire spread essential for effective mitigation and response. Physics-based simulators such as FARSITE offer high-fidelity predictions but are computationally intensive, limiting their applicability in real-time decision-making, while existing deep learning models often yield overly smooth predictions that fail to capture the complex, nonlinear dynamics of wildfire propagation. This study proposes an autoregressive conditional generative adversarial network (CGAN) for probabilistic wildfire spread prediction. By formulating the prediction task as an autoregressive problem, the model learns sequential state transitions, ensuring long-term prediction stability. Experimental results demonstrate that the proposed CGAN-based model outperforms conventional deep learning models in both overall predictive accuracy and boundary delineation of fire perimeters. These results demonstrate that adversarial learning allows the model to capture the strong nonlinearity and uncertainty of wildfire spread, instead of simply fitting the pixel average. Furthermore, the autoregressive framework facilitates systematic temporal forecasting of wildfire evolution. The proposed CGAN-based autoregressive framework enhances both the accuracy and physical interpretability of wildfire spread prediction, offering a promising foundation for time-sensitive response and evacuation planning.
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            <a href="https://www.alphaxiv.org/abs/2511.20994v1" target="_blank" rel="noopener noreferrer">
                GuardTrace-VL：通过迭代安全监督检测不安全的跨模态推理
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            GuardTrace-VL: Detecting Unsafe Multimodel Reasoning via Iterative Safety Supervision
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yuxiao Xiang, Junchi Chen, Zhenchao Jin, Changtao Miao, Haojie Yuan, Qi Chu, Tao...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于多模态模型的安全检测和监督，属于安全性和可靠性领域，这在您的无关主题列表中明确排除。虽然涉及多模态推理，但其核心关注点是安全监督而非推荐系统、搜索或广告中的技术应用，没有展示出在您关注领域中的潜在应用价值。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:49:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20994v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20994v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.AI</span><span class="category-tag">cs.CR</span></div>
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                    Multimodal large reasoning models (MLRMs) are increasingly deployed for vision-language tasks that produce explicit intermediate rationales. However, reasoning traces can contain unsafe content even when the final answer is non-harmful, creating deployment risks. Existing multimodal safety guards primarily evaluate only the input question and the final answer, neglecting the intermediate reasoning process. This oversight allows undetected harm, such as biased inferences or policy-violating use of visual context, to emerge during reasoning. We introduce GuardTrace-VL, a vision-aware safety auditor that monitors the full Question-Thinking-Answer (QTA) pipeline via joint image-text analysis, enabling detection of unsafe content as it emerges in the reasoning stage. To support training and evaluation, we construct the GuardTrace dataset, which is generated through diverse prompting strategies and refined via a MLRM- and human-based voting and verification pipeline. Furthermore, we propose a three-stage progressive training scheme combined with the data refinement process, enabling the model to learn nuanced and context-dependent safety preferences according to different risk levels. On our proposed test set covering both in-domain and out-of-domain scenarios, GuardTrace-VL model achieves an F1 score of 93.1% on unsafe reasoning detection tasks, representing a 13.5% improvement in F1 score compared to the previous strongest multimodal safety defense methods. The codes will be made publicly available.
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            <a href="https://www.alphaxiv.org/abs/2511.20991v1" target="_blank" rel="noopener noreferrer">
                波前约束的被动遮挡目标检测
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        <div class="mb-2 text-base text-gray-700">
            Wavefront-Constrained Passive Obscured Object Detection
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        <div class="mb-2 text-sm text-gray-600 italic">
            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Zhiwen Zheng, Yiwei Ouyang, Zhao Huang, Tao Zhang, Xiaoshuai Zhang, Huiyu Zhou, ...
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文标题涉及计算机视觉中的遮挡目标检测技术，专注于被动感知和波前约束方法。这与推荐系统、搜索或广告领域没有直接关联，也不涉及LLM、Transformer架构或异构数据建模等核心技术。该研究属于纯粹的视觉检测范畴，不符合当前关注的任何技术方向。</p>
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        <div class="flex flex-wrap items-center text-xs text-gray-500 pt-2 border-t border-gray-100">
                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:46:15
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20991v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20991v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span><span class="category-tag">cs.LG</span></div>
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                    Accurately localizing and segmenting obscured objects from faint light patterns beyond the field of view is highly challenging due to multiple scattering and medium-induced perturbations. Most existing methods, based on real-valued modeling or local convolutional operations, are inadequate for capturing the underlying physics of coherent light propagation. Moreover, under low signal-to-noise conditions, these methods often converge to non-physical solutions, severely compromising the stability and reliability of the observation. To address these challenges, we propose a novel physics-driven Wavefront Propagating Compensation Network (WavePCNet) to simulate wavefront propagation and enhance the perception of obscured objects. This WavePCNet integrates the Tri-Phase Wavefront Complex-Propagation Reprojection (TriWCP) to incorporate complex amplitude transfer operators to precisely constrain coherent propagation behavior, along with a momentum memory mechanism to effectively suppress the accumulation of perturbations. Additionally, a High-frequency Cross-layer Compensation Enhancement is introduced to construct frequency-selective pathways with multi-scale receptive fields and dynamically model structural consistency across layers, further boosting the model's robustness and interpretability under complex environmental conditions. Extensive experiments conducted on four physically collected datasets demonstrate that WavePCNet consistently outperforms state-of-the-art methods across both accuracy and robustness.
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            <a href="https://www.alphaxiv.org/abs/2511.20989v1" target="_blank" rel="noopener noreferrer">
                RefOnce：将参考信息蒸馏到原型记忆中以进行指代性伪装目标检测
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            RefOnce: Distilling References into a Prototype Memory for Referring Camouflaged Object Detection
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yu-Huan Wu, Zi-Xuan Zhu, Yan Wang, Liangli Zhen, Deng-Ping Fan
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        <div class="mb-2">
            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">这篇论文专注于计算机视觉中的伪装目标检测任务，属于纯粹的视觉领域研究。虽然提到了蒸馏和记忆机制，但这些技术是针对特定视觉问题设计的，没有显示出在推荐系统、搜索或广告领域的潜在应用价值。该工作与当前关注的核心领域进展和使能技术没有直接关联。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:42:52
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20989v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20989v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                    Referring Camouflaged Object Detection (Ref-COD) segments specified camouflaged objects in a scene by leveraging a small set of referring images. Though effective, current systems adopt a dual-branch design that requires reference images at test time, which limits deployability and adds latency and data-collection burden. We introduce a Ref-COD framework that distills references into a class-prototype memory during training and synthesizes a reference vector at inference via a query-conditioned mixture of prototypes. Concretely, we maintain an EMA-updated prototype per category and predict mixture weights from the query to produce a guidance vector without any test-time references. To bridge the representation gap between reference statistics and camouflaged query features, we propose a bidirectional attention alignment module that adapts both the query features and the class representation. Thus, our approach yields a simple, efficient path to Ref-COD without mandatory references. We evaluate the proposed method on the large-scale R2C7K benchmark. Extensive experiments demonstrate competitive or superior performance of the proposed method compared with recent state-of-the-arts. Code is available at https://github.com/yuhuan-wu/RefOnce.
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            <a href="https://www.alphaxiv.org/abs/2511.20986v1" target="_blank" rel="noopener noreferrer">
                基于双整流流的无反转风格迁移
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            Inversion-Free Style Transfer with Dual Rectified Flows
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            <i class="fa fa-user-circle-o text-gray-500 mr-1"></i>Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong, Xucheng Yin
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            <strong class="text-gray-700 text-sm"><i class="fa fa-thumbs-up text-green-500 mr-1"></i>个性化推荐理由:</strong>
            <p class="text-gray-600 text-sm mt-1">该论文专注于计算机视觉领域的风格迁移技术，属于纯粹的视觉生成任务。虽然整流流是生成模型的一种技术，但该工作没有展示与推荐系统、搜索或广告的潜在应用关联，也不涉及Transformer架构改进或多模态建模。</p>
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                <i class="fa fa-calendar-o mr-1"></i> 2025-11-26 02:28:51
                <span class="mx-2">|</span>
                <a href="https://arxiv.org/abs/2511.20986v1" target="_blank" rel="noopener noreferrer" class="text-primary hover:underline">
                    arXiv:2511.20986v1
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                <span class="mx-2">|</span>
                <div class="flex flex-wrap"><span class="category-tag">cs.CV</span></div>
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                 </summary> 
                 <div class="abstract-content mt-2 p-3 bg-gray-50 rounded-md text-sm text-gray-700">
                    Style transfer, a pivotal task in image processing, synthesizes visually compelling images by seamlessly blending realistic content with artistic styles, enabling applications in photo editing and creative design. While mainstream training-free diffusion-based methods have greatly advanced style transfer in recent years, their reliance on computationally inversion processes compromises efficiency and introduces visual distortions when inversion is inaccurate. To address these limitations, we propose a novel \textit{inversion-free} style transfer framework based on dual rectified flows, which tackles the challenge of finding an unknown stylized distribution from two distinct inputs (content and style images), \textit{only with forward pass}. Our approach predicts content and style trajectories in parallel, then fuses them through a dynamic midpoint interpolation that integrates velocities from both paths while adapting to the evolving stylized image. By jointly modeling the content, style, and stylized distributions, our velocity field design achieves robust fusion and avoids the shortcomings of naive overlays. Attention injection further guides style integration, enhancing visual fidelity, content preservation, and computational efficiency. Extensive experiments demonstrate generalization across diverse styles and content, providing an effective and efficient pipeline for style transfer.
                </div>
            </details>
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